{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 定义问题"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kaggle描述： It is your job to predict the sales price for each house. For each Id in the test set, you must predict the value of the SalePrice variable. \n",
    "\n",
    "预测房价 79个特征的数据集"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 获取数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kaggle:https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据预处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 导入环境库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python version: 2.7.13 |Anaconda custom (x86_64)| (default, Dec 20 2016, 23:05:08) \n",
      "[GCC 4.2.1 Compatible Apple LLVM 6.0 (clang-600.0.57)]\n",
      "pandas version: 0.23.3\n",
      "matplotlib version: 2.0.0\n",
      "NumPy version: 1.12.1\n",
      "SciPy version: 1.1.0\n",
      "IPython version: 5.1.0\n",
      "scikit-learn version: 0.19.1\n",
      "-------------------------\n"
     ]
    }
   ],
   "source": [
    "#load packages， 打印，便于可复现\n",
    "import sys #access to system parameters https://docs.python.org/3/library/sys.html\n",
    "print(\"Python version: {}\". format(sys.version))\n",
    "\n",
    "import pandas as pd #collection of functions for data processing and analysis modeled after R dataframes with SQL like features\n",
    "print(\"pandas version: {}\". format(pd.__version__))\n",
    "\n",
    "import matplotlib #collection of functions for scientific and publication-ready visualization\n",
    "print(\"matplotlib version: {}\". format(matplotlib.__version__))\n",
    "\n",
    "import numpy as np #foundational package for scientific computing\n",
    "print(\"NumPy version: {}\". format(np.__version__))\n",
    "\n",
    "import scipy as sp #collection of functions for scientific computing and advance mathematics\n",
    "print(\"SciPy version: {}\". format(sp.__version__)) \n",
    "\n",
    "import IPython\n",
    "from IPython import display #pretty printing of dataframes in Jupyter notebook\n",
    "print(\"IPython version: {}\". format(IPython.__version__)) \n",
    "\n",
    "import sklearn #collection of machine learning algorithms\n",
    "print(\"scikit-learn version: {}\". format(sklearn.__version__))\n",
    "\n",
    "#misc libraries\n",
    "import random\n",
    "import time\n",
    "\n",
    "\n",
    "#ignore warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "print('-'*25)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 导入数据模型与可视化库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Common Model Algorithms\n",
    "from sklearn import svm, tree, linear_model, neighbors, naive_bayes, ensemble, discriminant_analysis, gaussian_process\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "#Common Model Helpers\n",
    "from sklearn.preprocessing import OneHotEncoder, LabelEncoder\n",
    "from sklearn import feature_selection\n",
    "from sklearn import model_selection\n",
    "from sklearn import metrics\n",
    "\n",
    "#Visualization\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.pylab as pylab\n",
    "import seaborn as sns\n",
    "from pandas.tools.plotting import scatter_matrix\n",
    "\n",
    "#Configure Visualization Defaults\n",
    "#%matplotlib inline = show plots in Jupyter Notebook browser\n",
    "%matplotlib inline\n",
    "mpl.style.use('ggplot')\n",
    "sns.set_style('white')\n",
    "pylab.rcParams['figure.figsize'] = 12,8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 载入总览数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>...</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1201.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1452.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>730.500000</td>\n",
       "      <td>56.897260</td>\n",
       "      <td>70.049958</td>\n",
       "      <td>10516.828082</td>\n",
       "      <td>6.099315</td>\n",
       "      <td>5.575342</td>\n",
       "      <td>1971.267808</td>\n",
       "      <td>1984.865753</td>\n",
       "      <td>103.685262</td>\n",
       "      <td>443.639726</td>\n",
       "      <td>...</td>\n",
       "      <td>94.244521</td>\n",
       "      <td>46.660274</td>\n",
       "      <td>21.954110</td>\n",
       "      <td>3.409589</td>\n",
       "      <td>15.060959</td>\n",
       "      <td>2.758904</td>\n",
       "      <td>43.489041</td>\n",
       "      <td>6.321918</td>\n",
       "      <td>2007.815753</td>\n",
       "      <td>180921.195890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>421.610009</td>\n",
       "      <td>42.300571</td>\n",
       "      <td>24.284752</td>\n",
       "      <td>9981.264932</td>\n",
       "      <td>1.382997</td>\n",
       "      <td>1.112799</td>\n",
       "      <td>30.202904</td>\n",
       "      <td>20.645407</td>\n",
       "      <td>181.066207</td>\n",
       "      <td>456.098091</td>\n",
       "      <td>...</td>\n",
       "      <td>125.338794</td>\n",
       "      <td>66.256028</td>\n",
       "      <td>61.119149</td>\n",
       "      <td>29.317331</td>\n",
       "      <td>55.757415</td>\n",
       "      <td>40.177307</td>\n",
       "      <td>496.123024</td>\n",
       "      <td>2.703626</td>\n",
       "      <td>1.328095</td>\n",
       "      <td>79442.502883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>1300.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1872.000000</td>\n",
       "      <td>1950.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2006.000000</td>\n",
       "      <td>34900.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>365.750000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>7553.500000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1954.000000</td>\n",
       "      <td>1967.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2007.000000</td>\n",
       "      <td>129975.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>730.500000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>9478.500000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1973.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>383.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>163000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1095.250000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>11601.500000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>712.250000</td>\n",
       "      <td>...</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2009.000000</td>\n",
       "      <td>214000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1460.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>313.000000</td>\n",
       "      <td>215245.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>5644.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>857.000000</td>\n",
       "      <td>547.000000</td>\n",
       "      <td>552.000000</td>\n",
       "      <td>508.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>738.000000</td>\n",
       "      <td>15500.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>755000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Id   MSSubClass  LotFrontage        LotArea  OverallQual  \\\n",
       "count  1460.000000  1460.000000  1201.000000    1460.000000  1460.000000   \n",
       "mean    730.500000    56.897260    70.049958   10516.828082     6.099315   \n",
       "std     421.610009    42.300571    24.284752    9981.264932     1.382997   \n",
       "min       1.000000    20.000000    21.000000    1300.000000     1.000000   \n",
       "25%     365.750000    20.000000    59.000000    7553.500000     5.000000   \n",
       "50%     730.500000    50.000000    69.000000    9478.500000     6.000000   \n",
       "75%    1095.250000    70.000000    80.000000   11601.500000     7.000000   \n",
       "max    1460.000000   190.000000   313.000000  215245.000000    10.000000   \n",
       "\n",
       "       OverallCond    YearBuilt  YearRemodAdd   MasVnrArea   BsmtFinSF1  \\\n",
       "count  1460.000000  1460.000000   1460.000000  1452.000000  1460.000000   \n",
       "mean      5.575342  1971.267808   1984.865753   103.685262   443.639726   \n",
       "std       1.112799    30.202904     20.645407   181.066207   456.098091   \n",
       "min       1.000000  1872.000000   1950.000000     0.000000     0.000000   \n",
       "25%       5.000000  1954.000000   1967.000000     0.000000     0.000000   \n",
       "50%       5.000000  1973.000000   1994.000000     0.000000   383.500000   \n",
       "75%       6.000000  2000.000000   2004.000000   166.000000   712.250000   \n",
       "max       9.000000  2010.000000   2010.000000  1600.000000  5644.000000   \n",
       "\n",
       "           ...         WoodDeckSF  OpenPorchSF  EnclosedPorch    3SsnPorch  \\\n",
       "count      ...        1460.000000  1460.000000    1460.000000  1460.000000   \n",
       "mean       ...          94.244521    46.660274      21.954110     3.409589   \n",
       "std        ...         125.338794    66.256028      61.119149    29.317331   \n",
       "min        ...           0.000000     0.000000       0.000000     0.000000   \n",
       "25%        ...           0.000000     0.000000       0.000000     0.000000   \n",
       "50%        ...           0.000000    25.000000       0.000000     0.000000   \n",
       "75%        ...         168.000000    68.000000       0.000000     0.000000   \n",
       "max        ...         857.000000   547.000000     552.000000   508.000000   \n",
       "\n",
       "       ScreenPorch     PoolArea       MiscVal       MoSold       YrSold  \\\n",
       "count  1460.000000  1460.000000   1460.000000  1460.000000  1460.000000   \n",
       "mean     15.060959     2.758904     43.489041     6.321918  2007.815753   \n",
       "std      55.757415    40.177307    496.123024     2.703626     1.328095   \n",
       "min       0.000000     0.000000      0.000000     1.000000  2006.000000   \n",
       "25%       0.000000     0.000000      0.000000     5.000000  2007.000000   \n",
       "50%       0.000000     0.000000      0.000000     6.000000  2008.000000   \n",
       "75%       0.000000     0.000000      0.000000     8.000000  2009.000000   \n",
       "max     480.000000   738.000000  15500.000000    12.000000  2010.000000   \n",
       "\n",
       "           SalePrice  \n",
       "count    1460.000000  \n",
       "mean   180921.195890  \n",
       "std     79442.502883  \n",
       "min     34900.000000  \n",
       "25%    129975.000000  \n",
       "50%    163000.000000  \n",
       "75%    214000.000000  \n",
       "max    755000.000000  \n",
       "\n",
       "[8 rows x 38 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = pd.read_csv('train.csv')\n",
    "test_df = pd.read_csv('test.csv')\n",
    "\n",
    "'''\n",
    "预测SalePrice的值\n",
    "'''\n",
    "\n",
    "# train_df.columns \n",
    "# train_df.shape\n",
    "train_df.describe()\n",
    "# train_df.info()\n",
    "# print '%' * 40\n",
    "# test_df.info()\n",
    "# train_df.head(10)\n",
    "# train_df.info()\n",
    "# print train_df.describe(include = 'all')\n",
    "# print '%' * 40\n",
    "# test_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据4C分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据的：\n",
    "\n",
    "正确性：Reviewing the data, there does not appear to be any aberrant or non-acceptable data inputs.异常值\n",
    "\n",
    "完整性：NULL / NAN。删除/补全\n",
    "\n",
    "创造性：Feature engineering is when we use existing features to create new features to determine if they provide new signals to predict our outcome. For this dataset, we will create a title feature to determine if it played a role in survival.\n",
    "\n",
    "转变：类别数据 -> 独热编码"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 【完整性】 (缺失值)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://discuss.analyticsvidhya.com/t/what-should-be-the-allowed-percentage-of-missing-values/2456\n",
    "\n",
    "I：允许的missing values的数目，先定25%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "# # Missing Data的百分比\n",
    "# total = train_df.isnull().sum().sort_values(ascending=False)\n",
    "# percent = (train_df.isnull().sum()/train_df.isnull().count()).sort_values(ascending=False)\n",
    "# missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])\n",
    "# # print missing_data.head(20)\n",
    "\n",
    "# total2 = test_df.isnull().sum().sort_values(ascending=False)\n",
    "# percent2 = (test_df.isnull().sum()/test_df.isnull().count()).sort_values(ascending=False)\n",
    "# missing_data2 = pd.concat([total2, percent2], axis=1, keys=['Total', 'Percent'])\n",
    "# # print missing_data2.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
      "Train columns with null values:\n",
      "Index([u'Alley', u'FireplaceQu', u'PoolQC', u'Fence', u'MiscFeature'], dtype='object')\n",
      "%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
      "Test columns with null values:\n",
      "Index([u'Alley', u'FireplaceQu', u'PoolQC', u'Fence', u'MiscFeature'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "train_df.describe(include = 'all')\n",
    "print '%' * 40\n",
    "\n",
    "# print train_df.loc[:,'MasVnrType']\n",
    "\n",
    "# train_df['MasVnrType'] = train_df['MasVnrType'].replace(['None'],None )\n",
    "# train_df.loc[:,'MasVnrType'] =  train_df['MasVnrType'].apply(lambda x: None if x == 'None' else x)\n",
    "# print type(train_df.loc[1,'MasVnrType'])\n",
    "\n",
    "limit_missing_values = 0.25\n",
    "train_limit_missing_values = len(train_df) * limit_missing_values \n",
    "print \"Train columns with null values:\\n\", train_df.columns[train_df.isnull().sum().values > train_limit_missing_values]  # 依列为标准，column\n",
    "print '%'*40\n",
    "\n",
    "test_limit_missing_values = len(test_df) * limit_missing_values\n",
    "print \"Test columns with null values:\\n\", test_df.columns[test_df.isnull().sum().values > test_limit_missing_values]\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "丢弃过多空数据的列\n",
    "\n",
    "部分类别数据中的NA非未计数/计数误差，而是无（地下室，游泳池🏊等，填充字符串“None”）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 确定缺失列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['LotFrontage', 'Alley', 'MasVnrType', 'MasVnrArea', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2', 'Electrical', 'FireplaceQu', 'GarageType', 'GarageYrBlt', 'GarageFinish', 'GarageQual', 'GarageCond', 'PoolQC', 'Fence', 'MiscFeature']\n",
      "%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
      "['MSZoning', 'LotFrontage', 'Alley', 'Utilities', 'Exterior1st', 'Exterior2nd', 'MasVnrType', 'MasVnrArea', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1', 'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'BsmtFullBath', 'BsmtHalfBath', 'KitchenQual', 'Functional', 'FireplaceQu', 'GarageType', 'GarageYrBlt', 'GarageFinish', 'GarageCars', 'GarageArea', 'GarageQual', 'GarageCond', 'PoolQC', 'Fence', 'MiscFeature', 'SaleType']\n"
     ]
    }
   ],
   "source": [
    "missing_columns = list(train_df.columns[train_df.isnull().sum() != 0])\n",
    "print (missing_columns)\n",
    "# train_df[missing_columns].describe(include = 'all')\n",
    "print '%' * 40\n",
    "test_missing_columns = list(test_df.columns[test_df.isnull().sum() != 0])\n",
    "print test_missing_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "code_folding": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train missing numerical:  ['LotFrontage', 'MasVnrArea', 'GarageYrBlt'] \n",
      "\n",
      "Train missing category:  ['Alley', 'MasVnrType', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2', 'Electrical', 'FireplaceQu', 'GarageType', 'GarageFinish', 'GarageQual', 'GarageCond', 'PoolQC', 'Fence', 'MiscFeature'] \n",
      "\n",
      "Test missing numerical:  ['LotFrontage', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'BsmtFullBath', 'BsmtHalfBath', 'GarageYrBlt', 'GarageCars', 'GarageArea'] \n",
      "\n",
      "Test missing category:  ['MSZoning', 'Alley', 'Utilities', 'Exterior1st', 'Exterior2nd', 'MasVnrType', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2', 'KitchenQual', 'Functional', 'FireplaceQu', 'GarageType', 'GarageFinish', 'GarageQual', 'GarageCond', 'PoolQC', 'Fence', 'MiscFeature', 'SaleType'] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "train_missing_numerical = list(train_df[missing_columns].dtypes[train_df[missing_columns].dtypes != 'object'].index)\n",
    "train_missing_category = [i for i in missing_columns if i not in train_missing_numerical]\n",
    "\n",
    "test_missing_numerical = list(test_df[test_missing_columns].dtypes[test_df[test_missing_columns].dtypes != 'object'].index)\n",
    "test_missing_category = [i for i in test_missing_columns if i not in test_missing_numerical]\n",
    "\n",
    "print \"Train missing numerical: \", train_missing_numerical, \"\\n\"\n",
    "print \"Train missing category: \", train_missing_category,  \"\\n\"\n",
    "print \"Test missing numerical: \", test_missing_numerical, \"\\n\"\n",
    "print \"Test missing category: \", test_missing_category, \"\\n\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 类别特征缺失值填充"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 取众数的特征\n",
    "train_categories_Mode = ['Electrical']\n",
    "test_categories_Mode = ['MSZoning', 'Utilities', 'Exterior1st', 'Exterior2nd', 'KitchenQual', 'Functional', 'SaleType']\n",
    "\n",
    "train_categories_none = [i for i in train_missing_category if i not in train_categories_Mode]\n",
    "test_categories_none = [i for i in test_missing_category if i not in test_categories_Mode]\n",
    "\n",
    "\n",
    "# 通过字符串“None”填充 \n",
    "for category in train_categories_none:\n",
    "    train_df[category].fillna(\"None\", inplace=True)\n",
    "    \n",
    "for category in test_categories_none:\n",
    "    test_df[category].fillna(\"None\", inplace=True)\n",
    "    \n",
    "for category in train_categories_Mode:\n",
    "    train_df[category].fillna(train_df[category].mode()[0], inplace = True)\n",
    "\n",
    "for category in test_categories_Mode:\n",
    "    test_df[category].fillna(test_df[category].mode()[0], inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数值特征缺失值填充"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 取0的数值特征\n",
    "\n",
    "for col in ('GarageArea', 'GarageCars', 'GarageYrBlt'):\n",
    "    train_df[col] = train_df[col].fillna(0)\n",
    "    test_df[col] = test_df[col].fillna(0)\n",
    "    \n",
    "for col in ('BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF','TotalBsmtSF', 'BsmtFullBath', 'BsmtHalfBath'):\n",
    "    train_df[col] = train_df[col].fillna(0)\n",
    "    test_df[col] = test_df[col].fillna(0)\n",
    "    \n",
    "# 通过中位数填充\n",
    "for column in train_missing_numerical:\n",
    "    train_df[column].fillna(train_df[column].median(), inplace=True)\n",
    "\n",
    "for column in test_missing_numerical:\n",
    "    test_df[column].fillna(test_df[column].median(), inplace=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "print train_df.isnull().sum().max()\n",
    "print test_df.isnull().sum().max()\n",
    "# print test_df.loc[test_df.isnull()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 探索性数据分析 EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "感谢： https://www.kaggle.com/pmarcelino/comprehensive-data-exploration-with-python 提出的对数据进行探索的方法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 目标"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "1、In order to have some discipline in our analysis, we can create an Excel spreadsheet with the following columns:\n",
    "\n",
    "* <b>Variable</b> - Variable name.\n",
    "* <b>Type</b> -  There are two possible values for this field: 'numerical' or 'categorical'. \n",
    "* <b>Segment</b> - Identification of the variables' segment. We can define three possible segments: building, space or location. When we say 'building', we mean a variable that relates to the physical characteristics of the building (e.g. 'OverallQual'). When we say 'space', we mean a variable that reports space properties of the house (e.g. 'TotalBsmtSF'). Finally, when we say a 'location', we mean a variable that gives information about the place where the house is located (e.g. 'Neighborhood').\n",
    "* <b>Expectation</b> - Our expectation about the variable influence in 'SalePrice'. We can use a categorical scale with 'High', 'Medium' and 'Low' as possible values.\n",
    "    \n",
    "    重要，一一个一个阅读特征描述。是否影响我们购买房屋 -> 如果是，影响重要性？ -> 信息是否早已在其他变量中包含\n",
    "* <b>Conclusion</b> - Our conclusions about the importance of the variable, after we give a quick look at the data. We can keep with the same categorical scale as in 'Expectation'.\n",
    "* <b>Comments</b> - Any general comments that occured to us.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2、we can filter the spreadsheet and look carefully to the variables with 'High' 'Expectation'. Then, we can rush into some scatter plots between those variables and 'SalePrice', filling in the 'Conclusion' column which is just the correction of our expectations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "### 目标数据的【身材】 SalePrice （单变量分布）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Everything started in our Kaggle party, when we were looking for a dance partner. After a while searching in the dance floor, we saw a girl, near the bar, using dance shoes. That's a sign that she's there to dance. We spend much time doing predictive modelling and participating in analytics competitions, so talking with girls is not one of our super powers. Even so, we gave it a try:*\n",
    "\n",
    "*'Hi, I'm Kaggly! And you? 'SalePrice'? What a beautiful name! You know 'SalePrice', could you give me some data about you? I just developed a model to calculate the probability of a successful relationship between two people. I'd like to apply it to us!'*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      1460.000000\n",
       "mean     180921.195890\n",
       "std       79442.502883\n",
       "min       34900.000000\n",
       "25%      129975.000000\n",
       "50%      163000.000000\n",
       "75%      214000.000000\n",
       "max      755000.000000\n",
       "Name: SalePrice, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['SalePrice'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x109e0db50>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fFHAMC3W+OjW2NwZ1/nevJal/XI4QAADgT1HAMSz8/urvJQX3BcxeY2LH9C1HCAAA8Kco\n4BgWzjdeXwFlQtyEkPweyxECAID+UMAxLFxsuSiHzaGEkQkh+b3e5Qh/+8FvQ/J7AAAgclDAMSxU\nNldqsmtyUFdAuVHvcoRMQwEAAH+KAo5hobK5UinxKSH7PavFqmWzlumNijfU1dMVst8FAADhjwKO\nYeFiy8WQFnBJ+kLaF9TS0aLiquKQ/i4AAAhvFHBEveb2ZrV0tIS8gC+duVSx9lj9suyXIf1dAAAQ\n3ijgiHoXWy5KkqbETwnp746KGaWlM5fqP8r+Q4ZhhPS3AQBA+KKAI+pVNldKUkh2wfxTj9/1uKo9\n1TpZczLkvw0AAMITBRxRr7eAp7hCOwVFkv4i7S9ks9iYhgIAAPpQwBH1LjZfVGJsolwjXCH/7bGx\nY/XwtIcp4AAAoA8FHFGvsqVS08dMN+33H7/rcZU1lKmsocy0DAAAIHxQwBH1KpsrlTom1bTfX37X\ncknSf5T9h2kZAABA+KCAI6r1BHpU1VKl1NHmFfCU+BTdP+l+pqEAAABJFHBEuWpPtboCXaZOQZGu\nT0M5Xn1cV71XTc0BAADMRwFHVOtdAcXMKSjSH6eh7Lu4z9QcAADAfBRwRLWLzdc34TG7gM9Omq30\nxHQKOAAAoIAjulU2V8pqsYZ8F8xbefyux3Wi5oR8fp/ZUQAAgIko4IhqlS2Vcie4FWOLMTuKHp/9\nuLoD3fp93e/NjgIAAExEAUdUu9h8UdNHm/sCZq95k+Yp2Zms9668Z3YUAABgIgo4oprZa4DfyGqx\navH0xTpTd0b+Hr/ZcQAAgEko4IhaPr9PV31Xw6aAS9Jnp39WXYEuna0/a3YUAABgEgo4otaHLR9K\nUthMQZGk+yfdr1Exo1R6pdTsKAAAwCQUcEStcFkD/EYxthjNGT9Hp66eUk+gx+w4AADABBRwRK1w\nLOCSNHfCXPm6fLrQdMHsKAAAwAQUcEStiy0X5YxxatyocWZHuUlGUoZirDGshgIAwDBFAUfU6l0B\nxWKxmB3lJiPsI5SRlKH3r7wvwzDMjgMAAEJswAIeCAT07W9/W3l5eVqzZo2qqqpuOr5//37l5uYq\nLy9Pe/bsue05VVVVWrVqlVavXq3NmzcrEAhIkvbs2aMnnnhCK1as0IEDByRJHR0devbZZ7V69Wo9\n/fTTampq6rvGl7/8ZT355JP6yle+oubm5qG7G4gq4bQE4Z/KnJCp5o5mVV2rGngwAACIKgMW8L17\n98rv96uoqEjr16/X1q1b+451dXUpPz9fO3fuVGFhoYqKitTQ0NDvOfn5+Vq7dq127dolwzC0b98+\n1dfXq7CwULt379aOHTtUUFAgv9+vV199VWlpadq1a5eWL1+u7du3S5I2bdqktWvX6pVXXtHKlSv1\n4YcfBufOIKIZhqGLLeGzCc+fmpM8R1aLldVQAAAYhuwDDSgpKVFOTo4kae7cuTp9+nTfsYqKCrnd\nbiUkJEiSsrOzdeLECZWWlt7ynDNnzmj+/PmSpIULF+rw4cOyWq3KzMyUw+GQw+GQ2+1WWVmZSkpK\n9LWvfa1v7Pbt29XR0aGmpiYdOHBA27Zt0z333KNvfOMbQ3g7EC3qfHVq62oLyRNwX5dP5+rPDWrs\ntY5rkiSnw6lZY2ep9Eqplt+1PJjxAABAmBmwgHu9XsXFxfV9ttls6u7ult1ul9frlcvl6jvmdDrl\n9Xr7PccwjL75uE6nUx6P57bX6P2+d+y1a9d04cIF/eM//qPWrl2rb33rW/rlL3+pL37xi3d+JxBV\nLrZclBSaFVBa2lv0bu27gxqbOvqPeeZOmKuiM0W64r2iCXETghUPAACEmQGnoMTFxcnn8/V9DgQC\nstvttzzm8/nkcrn6Pcdqtd40Nj4+flDX6B2bkJAgp9OpBx98UBaLRYsWLbrpiTzQq3cJwuljwnMK\ninS9gEtiGgoAAMPMgAU8KytLxcXFkqTS0lKlpaX1HZsxY4aqqqrU0tIiv9+vkydPKjMzs99zMjIy\ndOzYMUlScXGx5s2bpzlz5qikpESdnZ3yeDyqqKhQWlqasrKydPDgwb6x2dnZGjlypKZNm6aTJ09K\nkk6cOKFZs2YN4e1AtOgt4NNGTzM3yG2MjR2rqQlTKeAAAAwzA05BWbJkiQ4fPqyVK1fKMAxt2bJF\nr732mtra2pSXl6eNGzfqqaeekmEYys3NVXJy8i3PkaQNGzZo06ZNKigoUGpqqpYuXSqbzaY1a9Zo\n9erVMgxD69at04gRI7Rq1Spt2LBBq1atUkxMjLZt2yZJ2rJli/7pn/5JPT09SklJYQ44buli80VN\njJuoUTGjzI5yW3MnzNWvyn+l5vZmjYkdY3YcAAAQAhYjyhciTk9PV3l5udkxEGKL/r9F8vf4dfir\nh/u+O1d/TocuHRrU+amjU1XZUhn0sbWeWj1/8Hmtvme1Hp728E1jc9w5mp00e1DXBQAA5hts72Qj\nHkSlcF4D/EYT4iYoaVSSztSfMTsKAAAIEQo4oo6/x6+PWj+6acWRcGWxWDQ7abbKGsrUE+gxOw4A\nAAgBCjiizqVrlxQwAmG9AsqNMsZlqLOns+/FUQAAEN0GfAkTCGd1vjo1tjXe9N07H73T979v3CCn\ndxOccJM+Ll0WWXSu4ZxmJbKqDwAA0Y4CjojW2Nb4Zy9WHr58/cXL6tZqHer547FwnZIyKmaUpo+Z\nrrP1Z/WF9C+YHQcAAAQZU1AQdZrammSRJaKW9csYl6EPWz6Uz+8beDAAAIhoFHBEnaaOJiWMSJDd\nGjn/wDM7abYMGSpvZMlMAACiHQUcUaexrVFjR401O8bHMn30dI20j9TZ+rNmRwEAAEFGAUfUaWpv\n0tjYyCrgNqtN6YnpOlt/VlG+NxYAAMMeBRxRJWAE1NzRHHEFXJIykjLU2N6o+rZ6s6MAAIAgooAj\nqng6PeoOdCsxNtHsKB9bRlKGJDENBQCAKEcBR1RpbL++JngkPgFPGpWkxNjEm9YuBwAA0YcCjqjS\n1N4kSRH5BNxisSgjKUNljWxLDwBANKOAI6pE8hNw6fo0lI7uDl1suWh2FAAAECQUcESVpvYmxdpj\nFRsTa3aUTyQ98Q/b0jMNBQCAqEUBR1Rpam+KyOknvZwOp6aNnqazDbyICQBAtKKAI6o0tTVF1Bb0\ntzI7abYuNl9Ua2er2VEAAEAQUMARVZo6IvsJuCTNHnd9W/oTNSfMjgIAAIKAAo6o0d7Vrrautojb\nhv5PTR89XTaLTe9fed/sKAAAIAgo4IgavUsQRuoKKL1ibDGakjBFpVdLzY4CAACCgAKOqBHJa4D/\nqdQxqTpdd1pdPV1mRwEAAEOMAo6o0bsGeFQU8NGp6uju0O/rfm92FAAAMMQo4Igaje2Nslvtco1w\nmR3ljqWOSZUkHb181OQkAABgqFHAETWa25s1ZuQYWS2R/5/12NixShqVpHeq3zE7CgAAGGKR31SA\nP2hsb4z4FzB7WSwW3Zd8H0/AAQCIQhRwRI2mtqaoKeCSdN+E+1TRXKE6X53ZUQAAwBCigCMqdAe6\nda3zWlS8gNnrvuT7JEnHPjpmchIAADCUKOCICs3tzTJkRNUT8HvG3yO71a6jHzENBQCAaEIBR1To\n24QnwnfBvNFI+0jNnTBX73zEi5gAAEQTCjiiQjRtwnOjByc/qOPVx9Ud6DY7CgAAGCIUcESF3k14\nxowcY3KSobVgygL5unw6XXfa7CgAAGCIUMARFZramxQ/Il4xthizowypB1MelCSmoQAAEEUo4IgK\nTe1NUTf9RJKmj56u8c7xvIgJAEAUoYAjKkTTJjw3slgsWpCygA15AACIIhRwRDzDMNTUHl2b8Nzo\nwZQHdaHpghrbGs2OAgAAhgAFHBHP4/eoO9AdlVNQJGlBygJJzAMHACBaUMAR8XqfDEfrE/B5k+bJ\nZrFRwAEAiBIUcES8aNyE50ZOh1NzkufwIiYAAFGCAo6I11fAR0ZnAZeuT0M5Vn1MPYEes6MAAIA7\nRAFHxGtsb9RI+0iNihlldpSgeSDlAXn9XpU1lJkdBQAA3CEKOCJe7wooFovF7ChBkz0xW5L03pX3\nTE4CAADuFAUcES+alyDslT4uXbH2WL1b+67ZUQAAwB2yDzQgEAjo+eefV3l5uRwOh1588UVNnTq1\n7/j+/fv18ssvy263Kzc3VytWrOj3nKqqKm3cuFEWi0WzZs3S5s2bZbVatWfPHu3evVt2u13PPPOM\nFi1apI6ODj333HNqbGyU0+nUSy+9pLFjx+qtt97SSy+9pIkTJ0qSnn32Wc2fPz94dwhhr6m9Salj\nUs2OEVR2q133TbiPAg4AQBQY8An43r175ff7VVRUpPXr12vr1q19x7q6upSfn6+dO3eqsLBQRUVF\namho6Pec/Px8rV27Vrt27ZJhGNq3b5/q6+tVWFio3bt3a8eOHSooKJDf79err76qtLQ07dq1S8uX\nL9f27dslSadPn9Zzzz2nwsJCFRYWUr6HOV+XT74uX9Q/AZekrAlZeu/KewoYAbOjAACAOzBgAS8p\nKVFOTo4kae7cuTp9+nTfsYqKCrndbiUkJMjhcCg7O1snTpzo95wzZ870FeaFCxfqyJEjOnXqlDIz\nM+VwOORyueR2u1VWVnbTNRYuXKijR4/2XePnP/+5Vq9era1bt6q7u3sIbwciTY2nRlL0rgF+o6yJ\nWWrtbFVlc6XZUQAAwB0YsIB7vV7FxcX1fbbZbH2l1+v1yuVy9R1zOp3yer39nmMYRt+Lck6nUx6P\n57bX6P2+d6wkfepTn9KmTZv0yiuvqK2tTbt3776Tvx8RrtZTK0lRuwvmjbImZkkS01AAAIhwAxbw\nuLg4+Xy+vs+BQEB2u/2Wx3w+n1wuV7/nWK3Wm8bGx8cP6hq9YyUpNzdXU6ZMkcVi0Wc/+1mdPXv2\nk/7tiALD6Qn43ePvVow1hgIOAECEG7CAZ2Vlqbi4WJJUWlqqtLS0vmMzZsxQVVWVWlpa5Pf7dfLk\nSWVmZvZ7TkZGho4dOyZJKi4u1rx58zRnzhyVlJSos7NTHo9HFRUVSktLU1ZWlg4ePNg3Njs7W4Zh\n6Atf+IKuXLkiSTp69KjuvvvuIbwdiDQ1nhpZLVYljEwwO0rQOWwO3Zt8LwUcAIAIN+AqKEuWLNHh\nw4e1cuVKGYahLVu26LXXXlNbW5vy8vK0ceNGPfXUUzIMQ7m5uUpOTr7lOZK0YcMGbdq0SQUFBUpN\nTdXSpUtls9m0Zs0arV69WoZhaN26dRoxYoRWrVqlDRs2aNWqVYqJidG2bdtksVj04osv6u/+7u80\ncuRIzZgxQytWrAj6TUL4qvXWamzsWFktw2NFzawJWfpl2S9vms4FAAAii8UwDMPsEMGUnp6u8vJy\ns2MgSLL/LVveLq/WL1g/4NjU0amqbBncC4zhMDbHnaPZSbNv+u5fT/yr/ttv/puq1lbJneAe1HUA\nAEBoDLZ3Do/HhohaNZ6aYTH/uxcvYgIAEPko4IhYXT1dqmurGxYroPSakzxHNouNAg4AQASjgCNi\nVXuqFTACw+oJeGxMrGYnzaaAAwAQwSjgiFhVLVWShscShDfKmphFAQcAIIJRwBGxLl27JGl4bMJz\no6wJWar11vZtQgQAACLLgMsQAuGq6tr1J+BjYseYnCQ4fF0+nas/92ff9z7x/1X5r/Tw1IclSYmj\nEjXeOT6k+QAAwCdDAUfEunTtkhJjE+WwOcyOEhQt7S23nGrS0d0hSXr9g9f71j/PcedQwAEAiBBM\nQUHEqrpWpUmuSWbHCLmR9pFKdib3TcEBAACRhQKOiFXVUqWJcRPNjmEKd4Jbl69dNjsGAAD4BCjg\niEiGYejStUua6BqeBXxKwhQ1tjfK6/eaHQUAAHxMFHBEpIa2BrV3tw/LKSiS+rah5yk4AACRhwKO\niNS7AsqwnYISf72AMw8cAIDIQwFHROotnsP1CbjT4VRibKIutVLAAQCINBRwRKTeXTCHawGXrk9D\n4Qk4AACRhwKOiFR1rUrOGKcSRiSYHcU07gS36nx1au9qNzsKAAD4GCjgiEiXrl3S1NFTZbFYzI5i\nmr4XMVt5ERMAgEhCAUdEqrpW1VdAh6vev59pKAAARBYKOCJSVUuVpiZMNTuGqeJHxGv0iNEsRQgA\nQIShgCMwjTCFAAAgAElEQVTi+Pw+NbY3DvsCLl3fkIeVUAAAiCwUcESc3ikXw30KinT9HtR6ankR\nEwCACEIBR8TpLeBTR/ME3J3gliFD5xvPmx0FAAAMEgUcEad3F0ymoPzxXwHONpw1OQkAABgsCjgi\nTlVLlWwWmya6huc29DcaM3KM4hxxOltPAQcAIFJQwBFxLrVeUkp8iuxWu9lRTGexWOROcFPAAQCI\nIBRwRJyqlirmf9/AHe/WhaYL6uzuNDsKAAAYBAo4Ig6b8NzMneBWd6BbZ+rPmB0FAAAMAgUcEaU7\n0K3q1mpewLxB7/8z8m7tuyYnAQAAg0EBR0Sp8dSox+ihgN9g3KhxcjlcFHAAACIEBRwRparl+hKE\nTEH5I4vFotnjZlPAAQCIEBRwRBQ24bm12Umz9f7V99Ud6DY7CgAAGAAFHBGldxOeKfFTTE4SXjKS\nMtTR3aGyhjKzowAAgAFQwBFRLl27pHGjxsnpcJodJaxkjMuQxIuYAABEAgo4IkrVtSpewLyFaaOn\naVTMKAo4AAARgAKOiFLZXKnUMalmxwg7NqtNcyfMpYADABABKOCIGD2BHn3Y8qGmj55udpSwlDUh\nS+9deU8BI2B2FAAAcBsUcESMGk+N/D1+noD3I2tilrx+rz5o+sDsKAAA4DYo4IgYlc2VkkQB70fW\nxCxJvIgJAEC4o4AjYlDAby8jKUMOm4MCDgBAmKOAI2JUNlfKarGyC2Y/Ymwxui/5Ph2vPm52FAAA\ncBsUcESMypZKuRPcirHFmB0lbC1IWaATNSfYERMAgDBGAUfEYAnCgS2YskBtXW06dfWU2VEAAEA/\nKOCIGJXNlUodTQG/nQUpCyRJRy8fNTkJAADoz4AFPBAI6Nvf/rby8vK0Zs0aVVVV3XR8//79ys3N\nVV5envbs2XPbc6qqqrRq1SqtXr1amzdvViBwfb3iPXv26IknntCKFSt04MABSVJHR4eeffZZrV69\nWk8//bSamppu+t0f/OAHWrdu3Z3fAUQEr9+rOl8dT8AH4E5wa5Jrko58dMTsKAAAoB8DFvC9e/fK\n7/erqKhI69ev19atW/uOdXV1KT8/Xzt37lRhYaGKiorU0NDQ7zn5+flau3atdu3aJcMwtG/fPtXX\n16uwsFC7d+/Wjh07VFBQIL/fr1dffVVpaWnatWuXli9fru3bt/f97sGDB/X2228P/d1A2LrYfFES\nK6AMxGKxaEHKAp6AAwAQxgYs4CUlJcrJyZEkzZ07V6dPn+47VlFRIbfbrYSEBDkcDmVnZ+vEiRP9\nnnPmzBnNnz9fkrRw4UIdOXJEp06dUmZmphwOh1wul9xut8rKym66xsKFC3X06PVCUVVVpaKiIn39\n618fwtuAcMcShIO3IGWBLrZc1BXvFbOjAACAWxiwgHu9XsXFxfV9ttls6u7u7jvmcrn6jjmdTnm9\n3n7PMQxDFoulb6zH47ntNXq/7x3r8/n0wgsv6IUXXpDNZrvDPx2RhAI+eA9NeUgS88ABAAhXAxbw\nuLg4+Xy+vs+BQEB2u/2Wx3w+n1wuV7/nWK3Wm8bGx8cP6hq9Yw8fPqz6+nqtW7dOW7Zs0TvvvKMf\n/vCHd/DnI1JUNlcqfkS8xsaONTtK2MuamCWHzaGjH1HAAQAIRwMW8KysLBUXF0uSSktLlZaW1nds\nxowZqqqqUktLi/x+v06ePKnMzMx+z8nIyNCxY8ckScXFxZo3b57mzJmjkpISdXZ2yuPxqKKiQmlp\nacrKytLBgwf7xmZnZ+tzn/ucfv3rX6uwsFD/8A//oAcffFB//dd/PbR3BGGpsuX6EoS9/4KC/o2w\nj1D2xGwducyLmAAAhCP7QAOWLFmiw4cPa+XKlTIMQ1u2bNFrr72mtrY25eXlaePGjXrqqadkGIZy\nc3OVnJx8y3MkacOGDdq0aZMKCgqUmpqqpUuXymazac2aNVq9erUMw9C6des0YsQIrVq1Shs2bNCq\nVasUExOjbdu2Bf1mIHxVNlcqIynD7BgRY0HKAr184mX5e/xy2BxmxwEAADewGIZhmB0imNLT01Ve\nXm52DNyBgBHQqO+O0rPzn9X//Nz/vOnYufpzOnTp0KCukzo6VZUtlVE5Nsedo9lJs/s+/+zsz/Sl\nn35Jx752TPMnzx/UNQAAwJ0ZbO9kIx6EvSveK+rs6eQFzI+h90VMpqEAABB+KOAIe6yA8vFNck2S\nO8HNi5gAAIQhCjjCHgX8k3loykMsRQgAQBiigCPsVTZXyiKLpo6eanaUiLIgZYEut17WR60fmR0F\nAADcgAKOsFfZXKmU+BRW8/iYFqQskMSGPAAAhBsKOMJeZXMl008+gbkT5irWHsuLmAAAhBkKOMIe\nBfyTibHFaN6kebyICQBAmKGAI6y1dbWp1ltLAf+EHprykN6tfVcd3R1mRwEAAH9AAUdY+7DlQ0ms\ngPJJLUhZoK5Al0pqSsyOAgAA/oACjrDGEoR3ZsGU6y9iHr582OQkAACgFwUcYY0CfmfGO8fr3vH3\n6o2KN8yOAgAA/oACjrBW2VwpZ4xTSaOSzI4SsZbNXKZDVYfk6fSYHQUAAIgCjjDXuwKKxWIxO0rE\nWjZrmboCXdp3cZ/ZUQAAgCjgCHMsQXjnPjXlU3I5XHr9wutmRwEAAKKAI4wZhkEBHwIxthg9kvqI\nXv/gdRmGYXYcAACGPQo4wtZV31W1d7dTwIfAspnLdLn1ss7WnzU7CgAAwx4FHGGLFVCGzrJZyyRJ\nr3/ANBQAAMxGAUfYooAPnZT4FN0z/h4KOAAAYYACjrBV0VQhSZo2epq5QaIEyxECABAeKOAIWxea\nLsid4NZI+0izo0SFZTOvL0e4/+J+s6MAADCsUcARtsoby5WWmGZ2jKjxKfenFOeIYxoKAAAmo4Aj\nLBmGofON55WemG52lKjhsDn0SOoj+s2F37AcIQAAJqKAIyxd9V1Va2crT8CHGMsRAgBgPgo4wtL5\nxvOSxBPwIbZsJssRAgBgNgo4wlJ5Q7kkKX0cBXwoTUmYoruT7qaAAwBgIgo4wlJ5Y7lG2EZoSvwU\ns6NEncdmPcZyhAAAmIgCjrB0vvG8ZiXOks1qMztK1Hls1mPqCnTxFBwAAJNQwBGWWIIweHLcOUqJ\nT9GPS39sdhQAAIYlCjjCTldPlyqbK3kBM0hsVpv+y5z/ojcq3lB1a7XZcQAAGHYo4Ag7F1suqjvQ\nzRPwIPry3C8rYARUeKrQ7CgAAAw7FHCEHZYgDL5ZibP0afen9aPSH7EpDwAAIUYBR9jpXYKQJ+DB\n9ZW5X9H5xvM6+tFRs6MAADCs2M0OAPyp843nlRibqMRRiWZHiRi+Lp/O1Z8b1NjEUYka7xyvL2V8\nSc++/qx+9N6P9NCUh4KcEAAA9KKAI+ywAsrH19Leondr3x3U2Bx3jsY7x8s1wqUvZXxJRWeK9C+P\n/oucDmeQUwIAAIkpKAhD5xvPswNmiHxl7lfk8Xv0i3O/MDsKAADDBgUcYaW1s1W13lpewAyRhVMX\nKnVMqn5U+iOzowAAMGxQwBFWLjRekMQLmKFisVj05fu+rAMfHtDF5otmxwEAYFhgDjjCSnnj9RVQ\neAIeOv917n/V5rc36/++/3+1+TObbzpW56tTY1vjoK7T+3InAAC4PQo4wsr5xvOyyKIZY2eYHWXY\ncCe49dnUz+rH7/9Ymx7eJKvlj/8w1tjWqEOXDg3qOr0vdwIAgNujgCMkBvsk9UT1CU1JmKKR9pEh\nSIVeT2c9rbyf5enX5b/W8ruWmx0HAICoRgFHSAz2Serp+tNyx7tDkAg3emL2E0odk6r83+XrL9P/\nUhaLxexIAABELQo4woZhGLrqvaoFKQsGvanMtY5rQU41PNitdj330HN65j+f0cGqg/rMtM+YHQkA\ngKg1YAEPBAJ6/vnnVV5eLofDoRdffFFTp07tO75//369/PLLstvtys3N1YoVK/o9p6qqShs3bpTF\nYtGsWbO0efNmWa1W7dmzR7t375bdbtczzzyjRYsWqaOjQ88995waGxvldDr10ksvaezYsTp58qRe\neuklWSwW3X///XruueeCeoMQOtc6r6mzp1NJo5IGPe84dXRqkFMNH1+e+2U9//bzyv9dPgUcAIAg\nGnAZwr1798rv96uoqEjr16/X1q1b+451dXUpPz9fO3fuVGFhoYqKitTQ0NDvOfn5+Vq7dq127dol\nwzC0b98+1dfXq7CwULt379aOHTtUUFAgv9+vV199VWlpadq1a5eWL1+u7du3S5K2bNmigoIC7dmz\nR6dOndLZs2eDdGsQale9VyVJU+KnmJxkeBppH6l1D67TmxVvDnpXTQAA8PEN+AS8pKREOTk5kqS5\nc+fq9OnTfccqKirkdruVkJAgScrOztaJEydUWlp6y3POnDmj+fPnS5IWLlyow4cPy2q1KjMzUw6H\nQw6HQ263W2VlZSopKdHXvva1vrG9BXzPnj2y2+3y+Xzyer0aNWrUUN0LmOyq73oBT4lPkbfLa3Ka\n6OXr8vU7xWfRtEWKc8TpW/u+pYKlBUzxAQAgCAYs4F6vV3FxcX2fbTaburu7Zbfb5fV65XK5+o45\nnU55vd5+zzEMo+/lLqfTKY/Hc9tr9H7fO1aS7Ha7SktL9fd///eaMWOGJkyYcIe3AOHiiveKYqwx\nGjdqnLzXKODB0tLectsn3J+e8mm9UfGGfnb2Z1qQsiCEyQAAGB4GnIISFxcnn8/X9zkQCMhut9/y\nmM/nk8vl6vccq9V609j4+PhBXaN3bK+5c+dq//79ysjI0A9/+MNP8ncjDNX56pQcl3zTOtQIvcXT\nF8tmtemtyrfMjgIAQFQasOlkZWWpuLhYklRaWqq0tD9uET5jxgxVVVWppaVFfr9fJ0+eVGZmZr/n\nZGRk6NixY5Kk4uJizZs3T3PmzFFJSYk6Ozvl8XhUUVGhtLQ0ZWVl6eDBg31js7OzZRiGVq9erWvX\nrv+zuNPpvKnUI7Jd9V5VsjPZ7BjDXsLIBD005SEd/ejooHfBBAAAgzfgFJQlS5bo8OHDWrlypQzD\n0JYtW/Taa6+pra1NeXl52rhxo5566ikZhqHc3FwlJyff8hxJ2rBhgzZt2qSCggKlpqZq6dKlstls\nWrNmjVavXi3DMLRu3TqNGDFCq1at0oYNG7Rq1SrFxMRo27Ztslgs+upXv6qnn35aDodDSUlJevHF\nF4N+kxB83YFuNbQ3aN7keWZHgaTPpX5Oh6oO6Rdlv9BnUz9rdhwAAKLKgAXcarXqhRdeuOm7GTP+\nuE344sWLtXjx4gHPkaTp06frJz/5yZ99v2LFCq1YseKm72JjY/X973//z8Y+8sgjeuSRRwaKjQjT\n0NaggBHgCXiYSHImad6keXrt/Gt6IOUBxTniBj4JAAAMCvM3EBaueK9IEgU8jDw26zF1dHdob+Ve\ns6MAABBVKOAICzWeGknSRNdEk5Og1yTXJC2culAHPjwgn9838AkAAGBQKOAICzWeGiXGJmqkfaTZ\nUXCDJ+99Up3dndp7kafgAAAMFQo4wkKtp5an32Fo+ujpypqYpf0X9/MUHACAIUIBh+l6Aj264rui\nSa5JZkfBLXx+1ufV0d2hfRf3mR0FAICoQAGH6erb6tUd6KaAh6nJ8ZOVNTFL+y7u4yk4AABDgAIO\n01W3VkuSJrsmm5wE/fmLWX/BU3AAAIYIBRymq/HWyCKLJsRNMDsK+jE5frKyJvAUHACAoUABh+lq\nPbVKGpUkh81hdhTcxufTrs8F339xv9lRAACIaBRwmK7GU8MKKBEgJT5Fc5Pnav+H+9XR3WF2HAAA\nIhYFHKbq6unSVd9VXsCMEMtmLVNbV5sOVh00OwoAABGLAg5T1fnqFDACFPAIMW30NM0eN1t7K/eq\nq6fL7DgAAEQkCjhM1bsFPQU8ciybuUytna06cvmI2VEAAIhIFHCYqtpTLavFqmRnstlRMEhpiWlK\nHZOqNyreUE+gx+w4AABEHAo4TFXrqdV453jF2GLMjoJBslgsWjZzmRrbG3Wi5oTZcQAAiDgUcJiq\nxlPD9JMIdO/4e5XiStFvP/itAkbA7DgAAEQUCjhM4+/xq76tXpPiKOCRxmKx6NFZj6rWW6vSK6Vm\nxwEAIKJQwGGaK94rMmTwBDxCZU/M1vhR4/X6B6/LMAyz4wAAEDEo4DANK6BENqvFqqUzl+rStUs6\n13DO7DgAAEQMCjhMU+Opkc1i03jneLOj4BN6MOVBjR45Wr/94LdmRwEAIGJQwGGaGk+NJsRNkM1q\nMzsKPiG71a5Hpj+i8sZyna47bXYcAAAiAgUcpmEFlOjwafenFWuP1Y73dpgdBQCAiEABhyk6ujvU\n2N5IAY8CsTGxenjaw3qr8i190PSB2XEAAAh7FHCYotZTK4kXMKPF4mmLZbPYtO3INrOjAAAQ9ijg\nMEWN9/oKKBPjJpqcBEMhYWSClt+1XD8q/ZGueq+aHQcAgLBGAYcpajw1irHGKMmZZHYUDJGvzP2K\n/D1+/Z/j/8fsKAAAhDUKOEzRuwKK1cJ/gtFi2uhpenz243r5xMvydHrMjgMAQNii/cAUNZ4aTXZN\nNjsGhtg3H/qmWjpa9O/v/rvZUQAACFsUcIRcW1ebWjpaNNHF/O9o80DKA3p46sMqeKdA/h6/2XEA\nAAhLFHCE3EetH0mSUuJTTE6CYNjwqQ36qPUj/eTUT8yOAgBAWKKAI+QuX7ssSZoSP8XkJAiGR2c+\nqswJmcr/Xb56Aj1mxwEAIOxQwBFyl1svK35EvBJGJpgdBUFgsVj0jwv/UR80faA9Z/aYHQcAgLBD\nAUfIXW69zPSTKLf8ruXKSMrQdw99VwEjYHYcAADCCgUcIdUd6Fatp1bueLfZURBEVotV38r5ls7U\nn9Gvyn5ldhwAAMIKBRwhVeupVY/Ro5QEnoBHuxV3r9DMsTP13UPflWEYZscBACBsUMARUpdbeQFz\nuLBb7fofn/4fKqkt0RsVb5gdBwCAsEEBR0hdvnZZDptD453jzY6CEPirOX8ld4Jb3yn+Dk/BAQD4\nAwo4Qqr3BUy2oB8eHDaHNnxqg45cPqKDVQfNjgMAQFigBSFkDMPQ5dbLTD8ZZr6a+VVNiJug7xR/\nx+woAACEBQo4QqaxvVEd3R0sQTjMjLSP1Dcf+qb2X9yvAxcPmB0HAADTUcARMpeuXZLEC5jD0TP3\nPyN3glvf3PtN1gUHAAx7FHCEzEetH8kiiybHTzY7CkJspH2kvrPoOzpZc5LdMQEAwx4FHCFzufWy\nJsRNkMPmMDsKTPDkvU9qTvIcfWv/t+Tv8ZsdBwAA0wxYwAOBgL797W8rLy9Pa9asUVVV1U3H9+/f\nr9zcXOXl5WnPnj23PaeqqkqrVq3S6tWrtXnzZgUC1/8pes+ePXriiSe0YsUKHThwfY5oR0eHnn32\nWa1evVpPP/20mpqaJElHjx5VXl6ennzySX39619Xe3v70N0NBNXla7yAOZzZrDa99MhLqmyu1A9O\n/sDsOAAAmGbAAr537175/X4VFRVp/fr12rp1a9+xrq4u5efna+fOnSosLFRRUZEaGhr6PSc/P19r\n167Vrl27ZBiG9u3bp/r6ehUWFmr37t3asWOHCgoK5Pf79eqrryotLU27du3S8uXLtX37dknS888/\nr5dfflmvvPKKpk6dqp/+9KdBujUYSs3tzWruaGYHzGFu6YylWjx9sb5T/B21draaHQcAAFMMWMBL\nSkqUk5MjSZo7d65Onz7dd6yiokJut1sJCQlyOBzKzs7WiRMn+j3nzJkzmj9/viRp4cKFOnLkiE6d\nOqXMzEw5HA65XC653W6VlZXddI2FCxfq6NGjkqTCwkKNGzdOktTd3a0RI0YM1b1AEJU1lkmS3PFu\nk5PATBaLRd975HtqaGvQ9w5/z+w4AACYYsAC7vV6FRcX1/fZZrOpu7u775jL5eo75nQ65fV6+z3H\nMAxZLJa+sR6P57bX6P2+d6wkjR9/fQfFN998U8eOHdPy5cs/8R+P0ClruF7AWYIQ2ZOytfKelSo4\nWqAaT43ZcQAACLkBC3hcXJx8Pl/f50AgILvdfstjPp9PLper33OsVutNY+Pj4wd1jd6xvX784x9r\n586d+vd//3eegEeIc/XnNHrkaLlGuAYejKj33cXfVXegW5sPbDY7CgAAITdgAc/KylJxcbEkqbS0\nVGlpaX3HZsyYoaqqKrW0tMjv9+vkyZPKzMzs95yMjAwdO3ZMklRcXKx58+Zpzpw5KikpUWdnpzwe\njyoqKpSWlqasrCwdPHiwb2x2drYk6V//9V918uRJ/fjHP9bYsWOH8FYgmMoay3gBE31Sx6Tq7+b/\nnXa8t0PHq4+bHQcAgJCyDzRgyZIlOnz4sFauXCnDMLRlyxa99tpramtrU15enjZu3KinnnpKhmEo\nNzdXycnJtzxHkjZs2KBNmzapoKBAqampWrp0qWw2m9asWaPVq1fLMAytW7dOI0aM0KpVq7Rhwwat\nWrVKMTEx2rZtmxoaGvTyyy8rIyNDTz/9tCRp2bJlWr16dXDvEu5Ie1e7LjZf1NKZS82OgjDy/Gee\n1+7Tu/XMfz6j4187LpvVZnYkAABCYsACbrVa9cILL9z03YwZM/r+9+LFi7V48eIBz5Gk6dOn6yc/\n+cmffb9ixQqtWLHipu9iY2P1/e9//8/G3vgSKCLDmfoz6jF6eAKOm8SPiNf/Wvq/tPLnK/WDkz/Q\nf5//382OBABASLARD4Luvdr3JLEFPf7cirtX6JHUR/QP+/9BV7xXzI4DAEBIDPgEHLhTpVdK5Yxx\nKnFUotlRECbqfHVqbGuUJP39g3+vv9z9l3r610/re0tuvTRh4qhEjXeOD2VEAACChgKOoCu9Wqq7\nxt0lq4V/cMF1jW2NOnTpUN/nz834nP7fhf+ntMQ0pY9L/7PxOe4cCjgAIGrQiBBU3YFulV4p1exx\ns82OgjD26MxHNW7UOO06vUvdgW6z4wAAEFQUcATVmbozautq073J95odBWHMYXNo5d0rdcV7RW9W\nvGl2HAAAgooCjqDqXeN5TvIck5Mg3N2bfK+yJmTpNxd+ozpfndlxAAAIGuaAI6iOVR/T2Nixcse7\ndenaJbPjIIh8XT6dqz83qLHXOq7d8vu8e/J09u2zeuX3r2jtA2tlsViGMiIAAGGBAo6gOl59XPMn\nz6dIDQMt7S16t/bdQY1NHZ16y+9HjxytJ2Y/oV2/36V3qt/RgpQFQxkRAICwwBQUBI3X79WZ+jOa\nP2m+2VEQQXLcOZoxZoZ+euan8nR6zI4DAMCQo4AjaEpqShQwApo/mQKOwbNarPqrOX+lju4O/ezs\nz8yOAwDAkKOAI2h6X8CkgOPjmuSapEdnPqp3qt/R2fqzZscBAGBIUcARNMeqj2n66OlKciaZHQUR\naNnMZUp2JuuV37+i9q52s+MAADBkKOAImuPVx/VAygNmx0CEirHF6Ml7n1RDW4N+UPIDs+MAADBk\nKOAIilpPrS63XuYFTNyR9HHpejDlQf2o9EcqaygzOw4AAEOCAo6g6J3/zRNw3Knc2bmKtcfq2def\nlWEYZscBAOCOUcARFMerj8tutStzQqbZURDh4kfE6+sPfF17K/fqp2d/anYcAADuGAUcQXGs+pjm\nJM9RbEys2VEQBVbevVKZEzK17o11rA0OAIh4FHAMuYAR0ImaE8z/xpCxWW3a/vntqvHU6IWDL5gd\nBwCAO8JW9Bhy5Q3lau1sZf43hoyvy6eEEQn64uwv6l+O/YtypuZo1thZtxybOCpR453jQ5wQAIDB\no4BjyLEBD4ZaS3uL3q19VwumLNDrH7yub7z5Da1fsF4Wi+XPxua4cyjgAICwxhQUDLlj1cfkcriU\nnphudhREmThHnJbftVwXmi7oRM0Js+MAAPCJUMAx5I5XH9f9k++XzWozOwqi0Kfdn5Y7wa1fnPuF\nOrs7zY4DAMDHRgHHkOro7tD7V9/nBUwEjdViVd7deWruaNYbFW+YHQcAgI+NAo4h9V7te+oOdPMC\nJoJq5tiZun/S/Xqz4k01tDWYHQcAgI+FAo4hxQuYCJXc2bmyWCz6+bmfmx0FAICPhQKOIfVO9Tua\n7JqsSa5JZkdBlBsTO0aPznxU79a+q/KGcrPjAAAwaBRwDBnDMHTww4PKmZpjdhQME0tSlygxNlFF\nZ4rUE+gxOw4AAINCAceQOd94XrXeWi2atsjsKBgmHDaHvpjxRVV7qvW7S78zOw4AAINCAceQOfDh\nAUmigCOkMidkKi0xTb8q/5U8nR6z4wAAMCAKOIbMgQ8PaLJrsmaOnWl2FAwjFotFq+5Zpfbudl7I\nBABEBAo4hoRhGHr7w7e1aPqiW24PDgTTJNckLZ2xVEc/Oqpj1cfMjgMAwG1RwDEkztafVZ2vjukn\nMM1jsx7TuFHj9E8H/4kdMgEAYc1udgBEB+Z/w2wOm0NP3vuk/vex/62tv9uqzZ/ZPKTXr/PVqbGt\ncVBjE0clarxz/JD+PgAgelDAMSTe/vBtuRPcmjZ6mtlRMIxlJGXo87M+ry2/26KV96xU+rj0Ibt2\nY1ujDl06NKixOe4cCjgAoF9MQcEd+//bu+/4pur9j+OvzI4kXZSyi22hRZZ0yLi29eK4OEBUFAQu\nuOACKsgURBC8Usa94hUHCijqA0TgFtR79cdlTxlCoYxSUCqUVbpoIUnTJk3O7w8kUmlpWQ2ln2cf\nfSQ5K5/z7cg7J9/zPS7FdaH/9x3S/1t43mv3vIavzpfBPwxGURRPlyOEEEJcRgK4uG4Hcg6Qb8uX\n7ifillDXty4zHpjBhmMb+HLvl54uRwghhLiMBHBx3dYf/a3/d5gEcHFrGBAzgHua3MOr/3uVQ3mH\nPF2OEEIIUYYEcHHd1h9bT3hgOKH+oZ4uRQgA1Co1i3oswkvjxeOLH+dc8TlPlySEEEK4SQAX18Xp\ncrIxc6N0PxG3nFD/UJJ7JpNRkEHf5X1xKS5PlySEEEIAEsDFddqbvZfC4kIJ4OKWlNg0kfe6vMcP\nv2Vtl9gAACAASURBVPzAm+vf9HQ5QgghBCDDEIrrJP2/xa3upbtfYs+ZPSRtTqJd/XY81fIpT5ck\nhBCilpMj4OK6rD+2nsg6kTQ0NfR0KUKUS6VS8dEjH9GxcUee+/Y59mTt8XRJQggharlKA7jL5eLN\nN9+kV69e9OvXj8zMzDLz161bR48ePejVqxdLly694jqZmZn07t2bPn36MGnSJFyuC30yly5dypNP\nPknPnj1Zv/7CEdXi4mKGDh1Knz59GDhwIGfPnnU/p9PpZNiwYWzatOnGtIK4JqWuUjZlbpLuJ+KW\n56X1YlnPZQR4B5DweQIL9y30dElCCCFqsUoD+Jo1a7Db7SxZsoRRo0Yxffp09zyHw8G0adOYP38+\nCxYsYMmSJeTl5VW4zrRp0xg+fDiLFi1CURTWrl1Lbm4uCxYsYPHixXz22We8++672O12vv76ayIj\nI1m0aBGPP/44s2fPBuD48eP07duX/fv336QmEVW1O2s3ZrtZArioERqaGvLTwJ+IaxhHv2/6MeA/\nAyhyFHm6LCGEELVQpQE8JSWFhIQEANq1a8eBAwfc8zIyMggNDcXf3x+9Xk9sbCw7d+6scJ20tDTa\nt28PQGJiIlu3bmXfvn1ER0ej1+sxmUyEhoZy6NChMttITExk27ZtABQVFZGUlESHDh1uYDOIa7H2\n17UA3HvHvR6uRIiqaWhqyJr+a3gj4Q3m75lPh087yDjhQgghql2lAdxisWA0Gt2PNRoNpaWl7nkm\nk8k9z2AwYLFYKlxHURT3pcoNBgNms/mK27g4/eKyAC1atCAiIuJ69lncIMsPLad9o/bUN9b3dClC\nVJlWrWXKfVNY0XcFZyxniJsbx8iVI9mXvc/TpQkhhKglKg3gRqMRq9XqfuxyudBqteXOs1qtmEym\nCtdRq9VllvXz86vSNi4uK24dxwqPsev0Lp66U0aUEDVTl2ZdSB2UyiPNH+HDnz7krk/uImZODLO2\nzyLXmuvp8oQQQtzGKg3gMTEx7pMdU1NTiYyMdM+LiIggMzOTwsJC7HY7u3btIjo6usJ1WrZsyY4d\nOwDYtGkTcXFxtG3blpSUFEpKSjCbzWRkZBAZGUlMTAwbN250LxsbG3tj91xcl2UHlwHQo2UPD1ci\nxLVr5NeIpU8v5fSo03zw8AeoVWqGrxxOg5kNSPg8gaRNSaScTpGL+AghhLihKh0H/MEHH+THH3/k\nmWeeQVEUpk6dyn//+1+Kioro1asX48aN48UXX0RRFHr06EG9evXKXQdg7NixTJw4kXfffZfw8HC6\ndOmCRqOhX79+9OnTB0VRGDFiBF5eXvTu3ZuxY8fSu3dvdDodM2fOvOmNIapuWfoyoutHEx4Y7ulS\nhLhuwb7BvNL+FV5p/woHcg6w+MBiVmasZML6CUxYP4G6vnVJCE2gTb02MuSmEEKI66ZSFEXxdBE3\nU1RUFIcPH/Z0GbeVk+dP0uRfTUi6L4nxCeOrtE56bjqbj2+u0rLhAeH8WvirLCvLXtPyCaEJ3Fn3\nzipv+0pyrDmszljNiiMrWJ6+HFupjbvq3cVDzR664pvPG1mDEEKImqOquVMuxCOu2vL05QD0uFO6\nn4jbW4ghhL5t+7LwyYWs7b+WrpFdOXL2CDN+nMHMrTM5cvaIp0sUQghRA0kAF1dtWfoyWoe0Jio4\nytOlCFFtArwD6BbZjan3T+Xplk+TU5TDO1vf4X9H/sdt/kGiEEKIG0wCuLgqZyxn2Jy5WUY/EbWW\nt9abB8If4K0/v0Vsw1i+OfQNn+z6BJvD5unShBBC1BCVnoQpxKW+Sf8GBUVGPxG3LKvDSnpuepWW\nreNbhxBDyDU9j7fWmwHRAwgPCCc5PZmpW6YyOHYwjfwaXdP2hBBC1B4SwMVVWZa+jKg6UbSq28rT\npQhRrkJbIbuzdldp2YTQhGsO4AAqlYr7w+8n1D+UubvnMv3H6bwY/SIJoQnXvE0hhBC3Pwngospy\nrblsOLaBcfHj3Fc0FaImu5qj5eeKz1U4r3md5kxImMDHuz5mTsocWtVtJaOgCCGEqJAEcFFl3x3+\nDqfilNFPxG3jao6Whwdcecx7f29/Xu3wKu/teI/hK4cTFhjGI80fuRFlCiGEuM3ISZiiypIPJhMe\nGE67+u08XYoQtyQfnQ+vdniVyDqRPLnkSVZlrPJ0SUIIIW5BEsBFlRTYClh7dC1P3fmUdD8R4gp8\ndb582u1TWgS3oPvi7qz9da2nSxJCCHGLkQAuqmTBvgWUukrp2aqnp0sR4pYX4B3Amv5raBbUjG5f\nd2PL8S2eLkkIIcQtRAK4qFSpq5T3tr/HPU3uIbZhrKfLEaJGCPYNZm3/tYT6h9J1UVf2ntnr6ZKE\nEELcIiSAi0p9k/4NRwuPMqrTKE+XIkSNEmIIYVW/VZi8THRZ2IWMsxmeLkkIIcQtQAK4uCJFUZi5\nbSbNgprxWNRjni5HiBon1D+UVX9dRamrlAcXPMhp82lPlySEEMLDJICLK9p6Yis7Tu1gRMcRaNQa\nT5cjRI10Z907WdF3BblFuXRZ2IUCW4GnSxJCCOFBMg64uKJ3tr1DkE8Qz7V77rJ5OdYc8ovyq7Sd\nK13ERIjbTXkX+DHqjcx6aBaDvx/MfV/ex7xu8zDoDdTxrXNdV+MUQghR80gAFxX6Jf8Xvjv0HW8k\nvIGvzvey+flF+Ww+vrlK26rsIiZC3E6udIGfF6JfYN7uefRM7smwDsPoEtFFArgQQtQy0gVFVOhf\n2/+FTqPj5fYve7oUIW4bMQ1i+FvM3zh+7jjvbntXuqMIIUQtJAFclCu/KJ8vUr/gr23+Sn1jfU+X\nI8RtJbpBNC/d/RJnLGfo/21/ssxZni5JCCFENZIALsr18a6PsZXaGNlppKdLEeK21DqkNUPbD+W0\n+TSJXyRy/NxxT5ckhBCimkgfcFFGjjWH4+eO897290gITUCtUl92MtlFcmKlENcnKjiKzx77jME/\nDKbTZ5348vEveSD8gRuy7as5SVpOBBVCiOolAVyUkV+Uz9jVYzlrO0unJp2ueJKlnFgpxPVrV78d\nm57bRK/kXjy44EGGth/K9Aeml3vi89W4mpOkE0ITJIALIUQ1ki4oooy9Z/ay/th67m16LxGBEZ4u\nR4ha4a76d7F70G6GtR/GBz99QMycGHae2unpsoQQQtwkEsCFm91pZ+KGiQR4B/B4i8c9XY4QtYqv\nzpdZD89idb/VWB1WOn3WiaH/N5TdWbtRFMXT5QkhhLiBJIALt2mbp3Hk7BH6tumLj87H0+UIUSs9\nEP4A+4fsp/9d/ZmTMofYubG0/rg10zZPu+oTNZ0uJzaHjcLiQs6XnMeluG5S1UIIIa6G9AEXABzM\nPUjS5iQebf4obeq18XQ5QtRqAd4BzO8+n3f+8g7/Tvs3C/YtYPy68YxfN54QQwjBvsEE+wZTx6cO\nAd4BWB1WztrOUmAr4KztLOdKzmEuMeNwOcpsV61S4+/lT4B3AAHeATT2a0yruq34U+M/eWhPhRCi\ndpIALnC6nAz4zwD8vPx4Pf510nLTPF2SEAII8gliUNwgBsUN4teCX/l32r85VniM3KJc8ory+Dn/\nZwqKCzDpTQT6BBJiCKFFcAv8vfwpcZaQY83BS+OFXqvH5XJRWFJIYXEh54rPkWXJIvVMKv/9+b98\nkvIJjzR/hIciHqJ7i+74efl5eteFEOK2JgFcMHvnbLad3MaCJxYQ5BPk6XKEEOUIDwzn+ejnqzy0\n4Lnic+zL2XfFZSx2CwdzD5JrzWV1xmoW7V+EQWegT5s+DIkbQnSD6BtRuhBCiD+QAF7LrT+6ntGr\nR/NQs4fo26Yvh/IOebokIUQFrmZowaoME2rUG2nfqD0JoQlEBUfx06mf+HT3pyzct5B5u+fRoVEH\nBscN5pnWz+Ct9b7e8oUQQvxGTsKsxfZk7aH74u40D2rOV09+hUql8nRJQggPsDqsHM47jL+XP6M6\njWL9s+t5Pf51cq25PP/d8zR6txEv//AymzM3k2PN8XS5QghR48kR8FrqyNkjPPTVQwT6BLLyryul\n64kQtVihrZDdWbvLTLsj4A5eu+c1DucfZs2va5i9azZzd8+lW2Q3Jv95Mm3rtfVQtUIIUfNJAK+F\nssxZdFnYBZfiYtVfV9HIr5GnSxJC3IJUKhUtglvQIrgFZyxnWHd0HSuOrOCbQ9/QsXFHBsYMpGer\nnhj1Rk+XKoQQNYoE8FqmsLiQh796mGxLNuufXU9UcJSnSxKiVrM6rKTnpldp2XPF525yNRWrb6xP\nnzZ9mHb/NLae2Mqnez7lxf+8yPD/Dad369482+5ZOjbuiFolPRuFEKIyEsBrkePnjvP44sc5mHuQ\nH/r8wN2N7vZ0SULUeuV1/6hIVU6svNkCvAMY9adRjOw0kq0ntjJv9zwW7FvA3N1zCTGE8GjzR+kW\n2Y0HIx6UI+NCCFEBCeC1QI41hxW/rGDEyhE4XA7ef/h9Gvs1LveomyePsAkhbn2XHrEP8gli7D1j\neSnuJTZmbmTDsQ0kH0zm89TP0al13N3wbhKaJtCxcUc6NOpAA1MDD1cvhBC3BgngtzlFUZi1fRbT\nf5xOiCGE4XHDUavUFQ5ldiscYRNC3LoqOmLv7+1P9xbd6RrZlSNnj7AvZx/Zlmze3fau+4qcTfya\n0K5+O1rVbUWrkFa0qtuKFsEt8NH5VPduCCGER0kAv43ZHDZe+r+X+CL1C+6qdxfPt3teXuiEEDeV\nRq0hKjiKqOAoYhrEoFVpSc9LZ1/2PvZl7yM9L50VR1ZQ6ioFQK1S09ivMZF1IokIjKBZUDOaBTYj\nLDAMvUZf7nPU8a1DiCGkOndLCCFuKAngt6kVv6xg6IqhZBRk8FLcS7Sp10ZOjhJCVKtCWyG/Fv4K\nQFhgGGGBYQA4XU6yrdlkmbM4ZT5FljmLw3mHWXd0HS7FBVwI5nV969LI1IgGpgY0NDWkoakhIYYQ\nOt/RWQK4EKJGkwB+mzlx7gTDVw5nefpyoupEsabfGhqaGlb56nlCCHGzadQad6COJRa40P3tcP5h\ncqw5nDafdgfzk+aT7DmzBwUFuBDMwwLCiGkQQ+uQ1u7uLM2CmqFVy0uaEKJmkP9Wt4kiRxEf/vQh\nf9/4d1yKi6n3TWVkp5F4ab2qPMSZEEJ4kk6jo5FfIxr5NeJufh+lye60k23J5rT5NKfNpylxlpCS\nlULywWR3MNdr9ETViXL3Lb8Yzo16I4XFhVV6funaIoSoLhLAa7gcaw4f/fQRs3fNJq8oj8eiHmPW\nQ7O4I+AOT5cmhBA3hF6jp4l/E5r4NwEgITSBO+veidVu5VDeIdJy0ziQc4C03DS2ndjG4gOL3evq\n1Dr8vf0J9A4k0Cfwwu1v9wO8AwjyCcKoN6JWqUkITZAALoSoFhLAa6j03HTe2/4eX+79khJnCY9F\nPcaoTqNIbJro6dKEEKJaGPQGYhvGEtswtsx0c4mZ9Lx00nLS2HJ8C/tz9lNQXEDG2QwKiwtxKs4y\ny2tUGgK8A2ga0JSoOlE09mvs/m7i14TGfo0JMYSgUWuqc/duqBxrDvlF+VVaVj4JEJeS352bQwJ4\nDaEoCilZKXx76Fu+PfQtablpeGm8ePauZxnRaQQtglt4ukQhhLglmLxMtG/UnvaN2tOxcccy58C4\nFBcWu4UCWwEFxQW/3xYXoCgKP536ieXpyylxlpTZplatpaGpYZlQ/seQXt9Y/5YI6S7FxfmS8xQW\nF3Ku+ByFxYXsz9nvHj5ShQq1Su3+1mv0eGm90Gv06DV6EkIT0NXX4avzRa/Ro1KpPLxHwpPyi/Kr\nfB6ZfIpUdZUGcJfLxeTJkzl8+DB6vZ4pU6bQtGlT9/x169bx0UcfodVq6dGjBz179qxwnczMTMaN\nG4dKpaJ58+ZMmjQJtVrN0qVLWbx4MVqtliFDhtC5c2eKi4sZM2YM+fn5GAwGZsyYQVBQEKmpqSQl\nJaHRaIiPj+eVV165qQ3kKRa7hb1n9rLnzB72ZO1hZcZKTplPoVFpSGyayMCYgfRu01t+0YUQtc6l\nFwOqzB8vLqZWqfHz8sPPy4+mNC0zL6ZBDAadAUVRKCgu4IzlDNnWbLIt2WRZssi2ZJNtzWb7ye2c\nsZy5LKRrVBrq+tYlxBhCkHcQgT6BBPkE0divMU39m7qf18/LD39vf7w0Xug0OrRqLTq1Do1ag8Pp\noMRZQklpCSXOEmwOG+dKzpUJ0xe/z1jOkFuUi9luxlxidt9a7BZ33/jrpVFpMOgN+Op8MegMl93X\nqDSoVWp8tD746HzK3HprvfHSeuFSXLgUl3s9p+LEpbhwupw4FSdOl5NSVyl2px2Hy4HdaaewuJDz\nJecpdZXicDoodZXiVJyoVWq0ai0alQaNWoNeo8fPyw+j3ohJb8LkZcKkN7mn+Xn54avzdY8CJkdo\nr8zpclJQXEB+UT75tnzyi/LZn7OfPVl7sJXaLvwslFJKXRe+FUW58PNQa9Cqtew8tZOwwDDq+NSh\njm+dy269td6e3sVbRqUBfM2aNdjtdpYsWUJqairTp0/n448/BsDhcDBt2jSSk5Px8fGhd+/e3Hff\nfezevbvcdaZNm8bw4cPp0KEDb775JmvXrqVdu3YsWLCAZcuWUVJSQp8+fbjnnnv4+uuviYyMZOjQ\nofzwww/Mnj2bCRMmMGnSJD744AOaNGnC3/72Nw4ePEjLli1vekNdC0VR3L+kpa5SHC4HVrsVq8OK\nxW7BYrdwrvgcWZYsssxZnDafJsuSxS9nf+GX/F/c/0Dr+NQhoWkCU1tM5dHmj1LHt46H90wIITyn\noosBledqLi5W3na1aq37xNBLhfmHsT/3QteWQluh+yh6ga2AwuJCMgoyMGdfCMMXxzy/UVSo8Pf2\ndwdLX60vPjofgnyC3OHXV3dhmq/WF1+dL80Cm5FfnI8KFS7FhYLiDsF2lx17qZ0SZwl2p939ZsHq\nsFLkKHK/bv3xcb4tnwJbAeeKz7nX/WP3nmt1aci+9NaluNwB3qW4cDgd7gs9Xam9fHQX2qS+oT4N\nTA3c5wME+QSVOSfg4huHK33rNDpUqFCpVO5PE270pwSKoqCgXHbrUlwVznO6nNhKbe6fUZGjyP19\n8WdX5CiisLjw94D9W8i+eFtYXFjhmzcVKnfY1ql17lGHnMqFN1BOl5MNyoYr/r4bdAZ3IA/wDqj0\n26g3uj+Z0al1v9/X/H5fo9LUyE9pKg3gKSkpJCQkANCuXTsOHDjgnpeRkUFoaCj+/v4AxMbGsnPn\nTlJTU8tdJy0tjfbt2wOQmJjIjz/+iFqtJjo6Gr1ej16vJzQ0lEOHDpGSksKAAQPcy86ePRuLxYLd\nbic0NBSA+Ph4tm7deksG8MTPE69q6D8VKkIMITQ0NaRV3Vb0bdOX6PrRRDeIppGpUY385RJCiNuV\nSqXCqDdi1Btp4tekwuUURSGmQQwhhhDOlZzjfMl5zpecd4fWS4/wlrpK0Wl0eGm83EePvbXeBHgH\n4O/l7w4lJi8TapWa9Nz0Kr/OhAeEoymsWveYiye5VsUfa3C6nO4j+HannVJXKSrVhZB6d8O7iQqO\ncgdqtUrtvq9Va90hS6vWcijvUJX3rYmpCQfzDlLkKMJWasPmsJW9X1pEkf1C+PTSemG2m8k8l+nu\nfnQj3yBdGsgvhnS1Su2+X154vviG6OK06mDUG8scnXYftfapQ5BPUJmj1gW2AtLz0vHR+lSaRRJC\nE7gj4A7O2s5eFu7zbfnkFeWRb8vnrO0shcWF/HL2F/cnOha75Zr359I2VqEi2DeYPYP2UM9Y75q3\nebNVGsAtFgtGo9H9WKPRUFpailarxWKxYDKZ3PMMBgMWi6XCdRRFcf/wDAYDZrP5itu4OP3SZS/d\nrsFg4MSJE5XuZFRUVKXL3AyRRF71OlaspP329RVf3YSqhBBCiIrNZOZN2e6t9poW+NtXbWb57SuT\nzBuyvWv53fH97etGS5x7aw9KUWkANxqNWK1W92OXy4VWqy13ntVqxWQyVbiOWq0us6yfn1+VtnGl\nZf38/K5Y/+HDhyvbRSGEEEIIIapNpdcmj4mJYdOmTQCkpqYSGfn7Ud2IiAgyMzMpLCzEbreza9cu\noqOjK1ynZcuW7NixA4BNmzYRFxdH27ZtSUlJoaSkBLPZTEZGBpGRkcTExLBx40b3srGxsRiNRnQ6\nHcePH0dRFLZs2UJcXNyNbREhhBBCCCFuIpWiKFfscHRxRJOff/4ZRVGYOnUqBw8epKioiF69erlH\nQVEUhR49etC3b99y14mIiODo0aNMnDgRh8NBeHg4U6ZMQaPRsHTpUpYsWYKiKAwaNIguXbpgs9kY\nO3Ysubm56HQ6Zs6cSd26dUlNTWXq1Kk4nU7i4+MZMWJEdbWVEEIIIYQQ163SAC6EEEIIIYS4cSrt\ngiKEEEIIIYS4cSSACyGEEEIIUY0kgAshhBBCCFGNKh2GUNx4F09SPXz4MHq9nilTptC0adPKV6zh\n9u7dyzvvvMOCBQvIzMxk3LhxqFQqmjdvzqRJk1Cr1SxdupTFixej1WoZMmQInTt3pri4mDFjxpCf\nn4/BYGDGjBkEBQWRmppKUlISGo2G+Ph4XnnlFQA+/PBDNmzYgFarZfz48bRt29bDe151DoeD8ePH\nc+rUKex2O0OGDKFZs2bSVhVwOp1MmDCBo0ePolKpeOutt/Dy8pL2qkB+fj5PPvkk8+fPR6vVSjtV\n4IknnnBfc6Jx48YMHjxY2qoCc+bMYd26dTgcDnr37k379u2lrcqxfPlyvvnmGwBKSkpIT09n0aJF\nTJ06VdrqDxwOB+PGjePUqVOo1Wrefvvt2/P/lSKq3cqVK5WxY8cqiqIoe/bsUQYPHuzhim6+uXPn\nKl27dlWefvppRVEUZdCgQcr27dsVRVGUiRMnKqtWrVJycnKUrl27KiUlJcr58+fd9+fPn6+8//77\niqIoyvfff6+8/fbbiqIoymOPPaZkZmYqLpdLGTBggJKWlqYcOHBA6devn+JyuZRTp04pTz75pGd2\n+BolJycrU6ZMURRFUQoKCpR7771X2uoKVq9erYwbN05RFEXZvn27MnjwYGmvCtjtduWll15S/vKX\nvyhHjhyRdqpAcXGx0r179zLTpK3Kt337dmXQoEGK0+lULBaL8v7770tbVcHkyZOVxYsXS1tVYPXq\n1cqwYcMURVGULVu2KK+88spt2VbSBcUDUlJSSEhIAKBdu3YcOHDAwxXdfKGhoXzwwQfux2lpabRv\n3x6AxMREtm7dyr59+4iOjkav12MymQgNDeXQoUNl2isxMZFt27ZhsViw2+2EhoaiUqmIj49n69at\npKSkEB8fj0qlomHDhjidTs6ePeuRfb4WDz30EK+++ipw4RLWGo1G2uoKHnjgAd5++20ATp8+jZ+f\nn7RXBWbMmMEzzzxDSEgIIH+DFTl06BA2m40XXniB/v37k5qaKm1VgS1bthAZGcnLL7/M4MGD+fOf\n/yxtVYn9+/dz5MgRevXqJW1VgbCwMJxOJy6XC4vFglarvS3bSgK4B1gsFvfHmwAajYbS0lIPVnTz\ndenSxX0FVbgQLlUqFQAGgwGz2YzFYsFkMrmXMRgMWCyWMtMvXfbSNqxsek1hMBgwGo1YLBaGDRvG\n8OHDpa0qodVqGTt2LG+//TbdunWT9irH8uXLCQoKcr8ogfwNVsTb25sXX3yRzz77jLfeeovRo0dL\nW1WgoKCAAwcOMGvWLGmrKpozZw4vv/wyIH+DFfH19eXUqVM8/PDDTJw4kX79+t2WbSV9wD3AaDRi\ntVrdj10uV5lwWhuo1b+/97Narfj5+V3WLlarFZPJVGb6lZb18/NDp9OVu42aJCsri5dffpk+ffrQ\nrVs3/vnPf7rnSVuVb8aMGYwePZqePXtSUlLini7tdcGyZctQqVRs27aN9PR0xo4dW+Yoj7TT78LC\nwmjatCkqlYqwsDACAgJIS0tzz5e2+l1AQADh4eHo9XrCw8Px8vLizJkz7vnSVmWdP3+eo0eP0rFj\nR0BeByvyxRdfEB8fz6hRo8jKyuLZZ5/F4XC4598ubSVHwD0gJiaGTZs2AZCamkpkZKSHK6p+LVu2\nZMeOHQBs2rSJuLg42rZtS0pKCiUlJZjNZjIyMoiMjCQmJoaNGze6l42NjcVoNKLT6Th+/DiKorBl\nyxbi4uKIiYlhy5YtuFwuTp8+jcvlIigoyJO7elXy8vJ44YUXGDNmDE899RQgbXUl3377LXPmzAHA\nx8cHlUpF69atpb3+4KuvvmLhwoUsWLCAO++8kxkzZpCYmCjtVI7k5GSmT58OQHZ2NhaLhXvuuUfa\nqhyxsbFs3rwZRVHIzs7GZrPRqVMnaasK7Ny5k06dOrkfy//28vn5+blDsL+/P6WlpbdlW8mVMD3g\n4igoP//8M4qiMHXqVCIiIjxd1k138uRJRo4cydKlSzl69CgTJ07E4XAQHh7OlClT0Gg0LF26lCVL\nlqAoCoMGDaJLly7YbDbGjh1Lbm4uOp2OmTNnUrduXVJTU5k6dSpOp5P4+HhGjBgBwAcffMCmTZtw\nuVy8/vrrxMXFeXjPq27KlCmsWLGC8PBw97Q33niDKVOmSFuVo6ioiNdff528vDxKS0sZOHAgERER\n8rt1Bf369WPy5Mmo1Wppp3LY7XZef/11Tp8+jUqlYvTo0QQGBkpbVeAf//gHO3bsQFEURowYQePG\njaWtKvDpp5+i1Wp57rnnAOR1sAJWq5Xx48eTm5uLw+Ggf//+tG7d+rZrKwngQgghhBBCVCPpgiKE\nEEIIIUQ1kgAuhBBCCCFENZIALoQQQgghRDWSAC6EEEIIIUQ1kgAuhBBCCCFENapdV38RQojbRGpq\nKjNnzqSwsBBFUahfvz5jx46lefPmFa4zbtw4mjdvzosvvljhMidPnuTBBx8sc30CRVHo37+/e2z6\nS61du5Zt27YxYcKE69shIYSoRSSACyFEDWO32xk0aBDz58+nVatWAHz33XcMHDiQtWvXotFo2Uci\nhQAAA8xJREFUrmv73t7efPfdd+7H2dnZdO3aldatW9OiRYsyy95///3cf//91/V8QghR20gAF0KI\nGsZms2E2mykqKnJPe+yxxzAajTidTqZNm8bevXuxWq0oisKUKVOIjY0ts42MjAySkpIoLCzE6XTS\nr1+/co9wA9SrV4+mTZty7NgxDh48SHJyMjabDaPRyBNPPMHKlSuZM2cOubm5TJo0iV9//RW1Ws0z\nzzxD//79MZvNJCUl8fPPP+NwOOjUqROvvfYaWq28BAkhaif57yeEEDWMv78/Y8aMYcCAAQQHBxMT\nE0OHDh149NFHSUtLIycnhyVLlqBWq5k7dy7z5s0rE8BLS0sZNmwY//jHP2jVqhVms5levXrRrFkz\ngoODL3u+PXv2cPz4ce666y62bdvGkSNHWLduHUajkeXLl7uXe+utt7jjjjuYPXs2ZrOZ3r17c++9\n9/LJJ5/QqlUrpk+fjtPpZNy4cXz++ecMHDiwWtpLCCFuNRLAhRCiBnr++ed5+umn2blzJzt37mTe\nvHnMmzeP5ORkhg8fzuLFizlx4gQ7duzAYDCUWffYsWMcP36c8ePHu6cVFxdz8OBBEhMTKS4upnv3\n7gA4nU4CAwP55z//SYMGDQCIiorCaDReVtPWrVsZM2YMACaTie+//x6ADRs2sH//fpKTk93PJYQQ\ntZkEcCGEqGFSUlLYs2cPAwYMoHPnznTu3JmRI0fSrVs31qxZw+zZs3n++ee5//77CQ8P5z//+U+Z\n9Z1OJ35+fmX6eefl5WEymcjNzb2sD/gf+fr6ljtdq9WiUqncj0+cOEFgYCAul4tZs2YREREBwPnz\n58ssJ4QQtY0MQyiEEDVMUFAQH3/8Mbt27XJPy83NxWaz8cMPP9C5c2f69OlDmzZtWLNmDU6ns8z6\nYWFheHl5uUN2VlYWXbt25cCBA9dVV6dOnVi2bBkAZrOZZ599lmPHjhEfH88XX3yBoijY7XaGDBnC\nwoULr+u5hBCiJpMj4EIIUcOEhYXx0Ucf8a9//YszZ87g5eWFyWTi73//O40aNWL06NF069YNjUZD\nXFwcq1atwuVyudfX6/XMnj2bpKQkPv30U0pLS3n11VeJjY3l5MmT11zXm2++yeTJk+nWrRuKojBo\n0CBat27NG2+8QVJSEt26dcPhcPCnP/2JAQMG3IimEEKIGkmlKIri6SKEEEIIIYSoLaQLihBCCCGE\nENVIArgQQgghhBDVSAK4EEIIIYQQ1UgCuBBCCCGEENVIArgQQgghhBDVSAK4EEIIIYQQ1UgCuBBC\nCCGEENXo/wFCtqJEaxMehgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x103de0c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train_df['SalePrice'], color='green')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* <b> 正偏态分布 </b> 数据主要集中在10w ~ 25w之间"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "偏度为 1.882876 \n",
      "峰度为 6.536282\n"
     ]
    }
   ],
   "source": [
    "print \"偏度为 %f \" % train_df['SalePrice'].skew()\n",
    "print \"峰度为 %f\"  % train_df['SalePrice'].kurt()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 目标数据的Buddies and interests  （两个变量之间的关系）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As soon as 'SalePrice' walked away, we went to Facebook. Yes, now this is getting serious. Notice that this is not stalking. It's just an intense research of an individual, if you know what I mean.\n",
    "\n",
    "According to her profile, we have some common friends. Besides Chuck Norris, we both know 'GrLivArea' and 'TotalBsmtSF'. Moreover, we also have common interests such as 'OverallQual' and 'YearBuilt'. This looks promising!\n",
    "\n",
    "To take the most out of our research, we will start by looking carefully at the profiles of our common friends and later we will focus on our common interests."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 同数值变量之间的关系 (散点图）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1104ef0d0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wKDLmAAAAgAUQmAMAAAAWQGAOAAAAWACBOQAAAGABBOYAAACABRCYAwAAABZAYA4AAABY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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1105408d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var = 'BsmtFinSF1' # 房屋居住面积\n",
    "\n",
    "# concat - Series默认行合并； axis = 1，列合并\n",
    "data = pd.concat([train_df['SalePrice'], train_df[var]], axis=1) \n",
    "# print dataFrame\n",
    "data.plot.scatter(x=var, y='SalePrice', ylim = (0, 800000)) # y轴限制"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "房屋价格同居住面积（平方英尺）线性相关"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x110590850>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FBQIB/dVf/ZXuv/9+LV26VLfeeuuABwoguWztgjkYu4CmqxP//DWXD8sgno8b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\niIiIiCgJMDAnIiIiIkoC/x/AgCbBAj5ueQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110762150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var = 'TotalBsmtSF'  # 房屋地下室的面积\n",
    "data = pd.concat([train_df['SalePrice'], train_df[var]], axis=1)\n",
    "data.plot.scatter(x = var, y = 'SalePrice', ylim = (0, 800000))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "房屋价格同地下室的面积成强线性（指数？）相关\n",
    "\n",
    "存在一些无地下室的房屋，房屋价格分布在200000以内"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 同类别变量之间的关系 （箱型图）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hQERERHwoHIiIiIgPhQMREYlIrWmL6XCjcCAiIhGptW0xHU6Cus7B/fffj9VqBeDGG29k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\nKByIiIiID4UDERER8fH/ATbMPbhO0BpNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11095da10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var = 'OverallQual'  # 房屋整体材料和光洁度的评估\n",
    "data = pd.concat([train_df['SalePrice'], train_df[var]], axis=1)\n",
    "f, ax = plt.subplots(figsize=(8, 6))\n",
    "fig = sns.boxplot(x=var, y=\"SalePrice\", data=data)  # 横坐标类别，纵坐标目标变量\n",
    "fig.axis(ymin=0, ymax=800000);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "房屋价格同房屋整体材料和光洁度的评估强相关，整体评估分数越高，房屋价格越高"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mdevW6S9/+Yv279+vffv26bXXXtOGDRu0YsUKLVq0qB9PBwAAAIB4Y/dGWn1h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\nbU+3XRDU7LkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110974fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var = 'YearBuilt'  # 房屋建造年份\n",
    "data = pd.concat([train_df['SalePrice'], train_df[var]], axis=1)\n",
    "f, ax = plt.subplots(figsize=(16, 8))  # 改变大小\n",
    "fig = sns.boxplot(x=var, y=\"SalePrice\", data=data)  # 横坐标类别，纵坐标目标变量\n",
    "fig.axis(ymin=0, ymax=800000);\n",
    "plt.xticks(rotation=90);               # x如果都水平放置，会粘到一起。x倾斜90度，减少占的位置"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "虽然斜率小，但是新房相对于老房来说房屋价格相对较高"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 总结"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1、房屋居住面积和房屋地下室的面积同房屋价格呈正相关\n",
    "\n",
    "2、房屋整体材料质量和建造年份同房屋价格相关。其中房屋整体材料质量同房屋价格相关性更强\n",
    "\n",
    "以上：特征选择，主观性\n",
    "\n",
    "The trick here seems to be the choice of the right features (feature selection) and not the definition of complex relationships between them (feature engineering).\n",
    "\n",
    "That said, let's separate the wheat from the chaff.\n",
    "\n",
    "以下：特征工程，客观性"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 客观分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  把一些数值特征转化为类别特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['MSSubClass', 'OverallCond', 'BedroomAbvGr', 'YrSold', 'MoSold']\n",
    "\n",
    "for col in cols:\n",
    "    train_df[col] = train_df[col].apply(str)\n",
    "    test_df[col] = test_df[col].apply(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* <b>相关性矩阵</b>\n",
    "* <b>房屋价格相关性矩阵</b>\n",
    "* <b>最相关变量散点图</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 整体相关性矩阵"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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M5ORkAM6dO0dsbCwTJ07EYDCwadMmnn/+ebp27cqXX35JXl5eqe1mzJhBWFgY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\nEzkKhUKhUCiUegpN5CgUCoVCoVDqKf8H6eV5Qpi3wqwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109e0dc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrmat = train_df.corr()\n",
    "f, ax = plt.subplots(figsize=(16, 8))\n",
    "sns.heatmap(corrmat, vmax= .8, square=True);   # vmax 颜色区别，最浅的颜色在0.8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "更加宏观\n",
    "\n",
    "1、“TotalBsmtSF” 同 “1stFlrSF”强相关，表达同样的信息\n",
    "\n",
    "2、“GarageCars”  同 “GarageArea”强相关，表达同样的信息\n",
    "\n",
    "3、验证“SalePrice” 同主观选择的特征相关，仍有部分特征需加入\"SalePrice\"预测"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 目标变量相关性矩阵 (SalePrice，放大热力图）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# help(corrmat.nlargest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# # cols = corrmat.nlargest(k, 'SalePrice').index\n",
    "# # print cols\n",
    "# len (train_df[cols].values)  # 数组，1460 * 10 | 转置 10 * 1460"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.605852184692\n",
      "0.319333802832\n",
      "0.613580551559\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7822600527979845"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "old_features = ['1stFlrSF', '2ndFlrSF', 'TotalBsmtSF']\n",
    "train_df['TotalSF'] = 0\n",
    "test_df['TotalSF'] = 0\n",
    "for i in old_features:\n",
    "    print train_df['SalePrice'].corr(train_df[i])\n",
    "    train_df['TotalSF'] += train_df[i]\n",
    "    test_df['TotalSF'] += test_df[i]\n",
    "train_df['SalePrice'].corr(train_df['TotalSF'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "corrmat = train_df.corr().abs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NEkuWw511G8cP63DfvYH101m4szNw376GVLAwRcrJALUWIVTWKGLK4868jete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sPQzRmP7dLZPDZrBn+IqMvGuH4liqatUTRtzaVbt/l86w7WfbcOW1q860F+2vpHJpXSsV0rOndo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\nS8Q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WO1VGdXmsDwBRreSpGb1Jv5PKt+98Rt1uzdAX4HQXxZye23ECgk6DK9vI9S4jydl7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\np6ZxNiEJURS513IyN6auInBSV4+YCR/VmdOvTeRUm3HIHTET0KIW3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lHo1M46VnqpyUpO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\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MEAiIOhBG7rMMrqw4iHPr+ky4vx0EcH7+Lmp2b/zGvgFcWBzM4N0zsa/hxOUfzpCTkvHm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\nLCzk3r17jB49miZNmhAeHs727dutrl9SntDQUGbOnGkuj0gk4sGDB9y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NSb0IugphH41a4MgsCOD1dxevthXpgWg8HF2bpfX/t3+xJSpyqhtatiMRYd887NuMePk76iVpuG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\nVKGdOw7oRFhUIx4d1Y+blzOoE9WIu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hdffOF8+1goFNKhQwdSUlKcZyq2b9+eixcv0q5d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\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tmyASi1EXah/wk5hICR+/nCyDWE44dZPs2GR2jl2C3KwUKVe2v55cjsLF3xPn9h6Y1bXi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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a18f855d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#saleprice correlation matrix\n",
    "k = 36   #number of variables for heatmap，热力图变量数量 \n",
    "\n",
    "# nlargest - 根据SalePrice列排序，返回前10个跟SalePrice相关性最高的行\n",
    "cols = corrmat.nlargest(k, 'SalePrice')['SalePrice'].index \n",
    "\n",
    "# cm = corrmat.loc[cols,cols] 同以下cm赋值相同\n",
    "# 训练集中取出目标列的样本，转置，计算10个特征之间的相关性\n",
    "cm = np.corrcoef(train_df[cols].values.T)\n",
    "sns.set(font_scale=1.25)\n",
    "hm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "82\n",
      "48\n"
     ]
    }
   ],
   "source": [
    "print len(train_df.columns)\n",
    "# print len(drop_columns)\n",
    "print len(train_df.dtypes[train_df.dtypes == 'object'].index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "1、确认主观特征选择变量'OverallQual'，'GrLivArea' ，'TotalBsmtSF'同房屋价格强相关\n",
    "\n",
    "2、因为“GarageCars” 同 “GarageArea”强相关，故选择同房屋价格相关性更高的“GarageCars”\n",
    "\n",
    "3、同上，选择“TotalBasmtSF”\n",
    "\n",
    "4、“TotRmsAbvGrd” 同“GrLivArea”强相关，选择“GrLivArea”\n",
    "\n",
    "5、\"YearBuilt\"【待】时间序列分析\n",
    "\n",
    "6、 \"FullBath\"浴室的品质，从箱型图中可以看出浴室的品质同房屋的价格呈正相关\n",
    "\n",
    "    同时存在一些无浴室的房屋"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 最相关变量散点图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "# cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "# #scatterplot\n",
    "# sns.set()\n",
    "# cols = ['SalePrice', 'OverallQual', 'GrLivArea', 'GarageCars', 'TotalBsmtSF', 'FullBath', 'YearBuilt']\n",
    "# sns.pairplot(train_df[cols], size = 2.5)\n",
    "# plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 特征分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 【正确性】"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "去除异常值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1、TotalBsmtSF 为0 = 房屋中没有地下室，数据正常\n",
    "\n",
    "2、故仅针对GrLivArea 做异常值去除"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "# olss = []\n",
    "# numerical_features = ['GrLivArea']\n",
    "# for feature in numerical_features:\n",
    "    \n",
    "#     # 计算25%分位点\n",
    "#     Q1 = np.percentile(train_df[feature], 25)\n",
    "    \n",
    "#     # 计算75%分位点\n",
    "#     Q3 = np.percentile(train_df[feature], 75)\n",
    "#     print Q3\n",
    "    \n",
    "#     # 异常阶（1.5倍四分位距）IQR\n",
    "#     step = 1.5 * (Q3 - Q1)\n",
    "    \n",
    "#     print \"Feature\" + feature + \"Outlines\"\n",
    "#     print train_df[ (train_df[feature] <= Q1 - step) | (train_df[feature] >= Q3 + step)][numerical_features]\n",
    "#     ols = features_train[ (train_df[feature] <= Q1 - step) | (train_df[feature] >= Q3 + step)].index.tolist()\n",
    "#     olss.append(ols)\n",
    "    \n",
    "# olss_new = [ii for i in olss for ii in i]\n",
    "# # print olss_new\n",
    "\n",
    "# # 列表方法 .count(i) 统计列表中某个元素出现的次数\n",
    "# more_than_one = list(set([i for i in olss_new if olss_new.count(i) > 1]))\n",
    "# more_than_two = list(set([i for i in olss_new if olss_new.count(i) > 2]))\n",
    "# print len(more_than_one), len(more_than_two)\n",
    "\n",
    "# ## 移除异常点\n",
    "# # features_train_new = features_train.drop(features_train.index[olss_new]).reset_index(drop = True)\n",
    "# # labels_train_new = labels_train.drop(features_train.index[olss_new]).reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IiEEp7IuIiIiIGJTCvoiIiIiIQSnsi4iIiIgYlMK+iIiIiIhBKeyLiIiIiBiUwr6IiIiI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6fW3bRXr17429/+hoULF+Kll17CuHHj8Oc//9nY91e/+hUuXryIRx99FG63G7/85S8xderUKN59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\nMewTEREREQUohn0iIiIiogD1/wrg+iZIcoF0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a18e26850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var = 'GrLivArea'\n",
    "data = pd.concat([train_df['SalePrice'], train_df[var]], axis=1)\n",
    "data.plot.scatter(x=var, y='SalePrice', ylim=(0,800000));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 从散点图中确定去除GrLivArea > 4000 且SalePrice < 200000的点\n",
    "train_df = train_df.drop(train_df[(train_df['GrLivArea'] > 4000) & (train_df['SalePrice'] < 200000)].index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 【创造性】"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "删除相关性较弱的特征"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "所有类别特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'MSSubClass', u'MSZoning', u'Street', u'Alley', u'LotShape',\n",
       "       u'LandContour', u'Utilities', u'LotConfig', u'LandSlope',\n",
       "       u'Neighborhood', u'Condition1', u'Condition2', u'BldgType',\n",
       "       u'HouseStyle', u'OverallCond', u'RoofStyle', u'RoofMatl',\n",
       "       u'Exterior1st', u'Exterior2nd', u'MasVnrType', u'ExterQual',\n",
       "       u'ExterCond', u'Foundation', u'BsmtQual', u'BsmtCond', u'BsmtExposure',\n",
       "       u'BsmtFinType1', u'BsmtFinType2', u'Heating', u'HeatingQC',\n",
       "       u'CentralAir', u'Electrical', u'BedroomAbvGr', u'KitchenQual',\n",
       "       u'Functional', u'FireplaceQu', u'GarageType', u'GarageFinish',\n",
       "       u'GarageQual', u'GarageCond', u'PavedDrive', u'PoolQC', u'Fence',\n",
       "       u'MiscFeature', u'MoSold', u'YrSold', u'SaleType', u'SaleCondition'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.dtypes[(train_df.dtypes == 'object')].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DS5vX1tby8MMPc+nSJRoaGkhJSeH06dNER0ezceNGYzO0YNRdljZvamrkySef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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a18e7c210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var = 'BldgType'  # 房屋整体材料和光洁度的评估\n",
    "data = pd.concat([train_df['SalePrice'], train_df[var]], axis=1)\n",
    "f, ax = plt.subplots(figsize=(8, 6))\n",
    "fig = sns.boxplot(x=var, y=\"SalePrice\", data=data)  # 横坐标类别，纵坐标目标变量\n",
    "fig.axis(ymin=0, ymax=800000);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 【倾斜数据处理】"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 预测值倾斜，对数处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* <b> 偏度 </b>  正态分布的偏度为0。若数据分布是对称的，偏度 = 0。\n",
    "\n",
    "     若偏度 > 0，分布为右偏，即分布有一条长尾在右；\n",
    "     \n",
    "     若偏度 < 0，分布为左偏，即分布有一条长尾在左。偏度的绝对值越大，说明分布的偏移程度越严重。\n",
    "\n",
    "\n",
    "* <b> 峰度 </b>  正态分布的峰度为0。\n",
    "\n",
    "    当峰度 > 0，它相比于正态分布要更陡峭或尾部更厚。\n",
    "    \n",
    "    峰度系数 < 0, 它相比于正态分布更平缓或尾部更薄。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normality - When we talk about normality what we mean is that the data should look like a normal distribution. This is important because several statistic tests rely on this (e.g. t-statistics). In this exercise we'll just check univariate normality for 'SalePrice' (which is a limited approach). Remember that univariate normality doesn't ensure multivariate normality (which is what we would like to have), but it helps. Another detail to take into account is that in big samples (>200 observations) normality is not such an issue.\n",
    "\n",
    "However, if we solve normality, we avoid a lot of other problems (e.g. heteroscedacity) so that's the main reason why we are doing this analysis.\n",
    "\n",
    "正态分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1ad84250>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VGeraldHmeHPttbYMQ1qzhlFnALGH4Awg4Rw9Kh08aKprV1bUiDfdunm69FJX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\nJHAWQgghhBCiCf4f014aK3MF8AAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1ad804d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_df['SalePrice'] = np.log1p(train_df['SalePrice'])\n",
    "sns.distplot(train_df['SalePrice'], color='blue')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.121579760503\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.80475079174189723"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print train_df['SalePrice'].skew()\n",
    "train_df['SalePrice'].kurt()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 【转化性】\n",
    "Label Encoding 一些可以包含信息的类别特征\n",
    "\n",
    "**表达类别特征之间的高低顺序Excellent > Good > bad** \n",
    "\n",
    "每单位变化跟目标变量并没有关系！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape all_data: (1458, 82)\n",
      "Shape all_data: (1459, 81)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "# categories = [i for i in train_df.columns if i not in cols]\n",
    "# process columns, apply LabelEncoder to categorical features\n",
    "\n",
    "categories = ['FireplaceQu', 'BsmtQual', 'BsmtCond', 'GarageQual', 'GarageCond', \n",
    "        'ExterQual', 'ExterCond','HeatingQC', 'PoolQC', 'KitchenQual', 'BsmtFinType1', \n",
    "        'BsmtFinType2', 'Functional', 'Fence', 'BsmtExposure', 'GarageFinish', 'LandSlope',\n",
    "        'LotShape', 'PavedDrive', 'Street', 'Alley', 'CentralAir', 'MSSubClass', 'OverallCond', \n",
    "        'YrSold', 'MoSold', 'BedroomAbvGr']\n",
    "\n",
    "for c in categories:\n",
    "    lbl = LabelEncoder() \n",
    "    train_df[c] = lbl.fit_transform(list(train_df[c].values))\n",
    "    test_df[c] = lbl.fit_transform(list(test_df[c].values))\n",
    "\n",
    "\n",
    "# shape        \n",
    "print 'Shape all_data: {}'.format(train_df.shape)\n",
    "print 'Shape all_data: {}'.format(test_df.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_features = list(train_df.dtypes[train_df.dtypes != 'object'].index)\n",
    "numeric_features.remove('SalePrice')\n",
    "len(numeric_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 转化后所有特征标准化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# box-cox变换\n",
    "from scipy.special import boxcox1p\n",
    "# skewed_features = list(skewness)\n",
    "lam = 0.15\n",
    "for feature in numeric_features:\n",
    "    #all_data[feat] += 1\n",
    "    train_df[feature] = boxcox1p(train_df[feature], lam)\n",
    "    test_df[feature] = boxcox1p(test_df[feature], lam)\n",
    "    \n",
    "#all_data[skewed_features] = np.log1p(all_data[skewed_features])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(train_df[numeric_features])\n",
    "train_df[numeric_features] = scaler.transform(train_df[numeric_features])\n",
    "test_df[numeric_features] = scaler.transform(test_df[numeric_features])\n",
    "\n",
    "# scaler = MinMaxScaler()\n",
    "# scaler.fit(train_df[numeric_features])\n",
    "# train_df[numeric_features] = scaler.transform(train_df[numeric_features])\n",
    "# test_df[numeric_features] = scaler.transform(test_df[numeric_features])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "category_columns = train_df.dtypes[train_df.dtypes == 'object'].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train\n",
      "Utilities\n",
      "AllPub    1457\n",
      "NoSeWa       1\n",
      "Name: Utilities, dtype: int64\n",
      "Test\n",
      "Utilities\n",
      "AllPub    1459\n",
      "Name: Utilities, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "for i in category_columns:\n",
    "    if len(train_df[i].value_counts().index) <= 2:\n",
    "        print  \"Train\\n\" +  i\n",
    "        print train_df[i].value_counts()\n",
    "        \n",
    "    if len(test_df[i].value_counts().index) <= 2:\n",
    "        print \"Test\\n\" + i\n",
    "        print test_df[i].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "drop_columns = 'Utilities'\n",
    "train_df = train_df.drop(drop_columns, axis=1)\n",
    "test_df = test_df.drop(drop_columns, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 转化性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "features_train = train_df.drop(['SalePrice'], axis=1)\n",
    "labels_train = train_df['SalePrice']\n",
    "features_test = test_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 独热编码其他与顺序无关的特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "code_folding": [
     4
    ],
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "features_train = pd.get_dummies(features_train)\n",
    "features_test = pd.get_dummies(test_df)\n",
    "\n",
    "missing_cols = set(features_train.columns) - set(features_test.columns)\n",
    "for column in missing_cols:\n",
    "    features_test[column] = 0\n",
    "    \n",
    "# 保证测试集columns的顺序同训练集columns相同，特别重要！！！！！！\n",
    "features_test = features_test[features_train.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index([u'Id', u'MSSubClass', u'LotFrontage', u'LotArea', u'Street', u'Alley',\n",
      "       u'LotShape', u'LandSlope', u'OverallQual', u'OverallCond',\n",
      "       ...\n",
      "       u'SaleType_ConLw', u'SaleType_New', u'SaleType_Oth', u'SaleType_WD',\n",
      "       u'SaleCondition_Abnorml', u'SaleCondition_AdjLand',\n",
      "       u'SaleCondition_Alloca', u'SaleCondition_Family',\n",
      "       u'SaleCondition_Normal', u'SaleCondition_Partial'],\n",
      "      dtype='object', length=221)\n"
     ]
    }
   ],
   "source": [
    "print features_train.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 分割训练和测试数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1458\n"
     ]
    }
   ],
   "source": [
    "# from sklearn.model_selection import train_test_split\n",
    "\n",
    "# # 分割features_train 和 labels_train, 测试集大小 = 20%，状态：随机，可复现\n",
    "# # 顺序：测试特征，训练特征，测试目标，训练目标\n",
    "\n",
    "# X_train, X_test, y_train, y_test = train_test_split(features_train, labels_train, test_size = 0.2, random_state = 42)\n",
    "\n",
    "# 输出数量观察\n",
    "\n",
    "X_train = features_train\n",
    "y_train = labels_train\n",
    "print len(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模型实现与融合"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 分析问题，确定模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "问题为回归问题，可用模型：线性回归，决策树（C&RT决策树），随机森林，GBDT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 首先用简单的模型进行试验，观察评分"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "回归模型评分指标包括：\n",
    "\n",
    "1、SSE误差平方和， RMSE\n",
    "\n",
    "2、R-square（决定系数）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# # 导入算法模型和评分标准 \n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.linear_model import LinearRegression, ElasticNet\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import fbeta_score, make_scorer, r2_score ,mean_squared_error\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.svm import SVR\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn.model_selection import KFold, cross_val_score, train_test_split\n",
    "\n",
    "# def rmsle_cv(model, train, value):\n",
    "#     kf = KFold(5, shuffle=True, random_state=42).get_n_splits(train.values)\n",
    "#     rmse= np.sqrt(-cross_val_score(model, train.values, y_train, scoring=\"neg_mean_squared_error\", cv = kf))\n",
    "#     return rmse.mean()\n",
    "\n",
    "# # for key_num in range(10,25,3):\n",
    "\n",
    "# #     cols_model = cols[:key_num]\n",
    "# #     drop_columns = [i for i in corrmat.columns if i not in cols_model]\n",
    "# #     print drop_columns , \"\\n\"\n",
    "# #     X_train_model = X_train.drop(drop_columns, axis=1)\n",
    "\n",
    "# #         # 初始化,确定随机状态，可复现\n",
    "# #     reg1 = DecisionTreeRegressor(random_state = 42)\n",
    "# #     reg2 = LinearRegression()\n",
    "# #     reg3 = RandomForestRegressor(random_state = 42)\n",
    "# #     reg4 = XGBRegressor()\n",
    "# #     reg5 = Lasso(alpha=0.001, random_state= 42)\n",
    "# #     reg6 = SVR(kernel = 'rbf')\n",
    "# #     reg7 = ElasticNet(alpha=0.0005, l1_ratio=.9, random_state=3)\n",
    "\n",
    "# #     # 建立字典，收集学习器的效果\n",
    "# #     # 学习，收集预测得分\n",
    "# #     results = {}\n",
    "# #     for reg in [reg1, reg2, reg3, reg4, reg5, reg6, reg7]:\n",
    "# #         # 回归器的名称\n",
    "# #         reg_name = reg.__class__.__name__\n",
    "# # #         reg.fit(X_train_model, y_train)\n",
    "# # #         pred_test = reg.predict(X_test_model)\n",
    "# # #         results[reg_name] = rmse(y_test, pred_test)\n",
    "# #         results[reg_name] = rmsle_cv(reg, X_train_model, y_train)\n",
    "# #     print key_num, \"---\", results\n",
    "# #     print '\\n'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Id', 'BsmtFinSF2', 'BsmtUnfSF', 'LowQualFinSF', 'BsmtFullBath', 'BsmtHalfBath', 'KitchenAbvGr', 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MiscVal'] \n",
      "\n",
      "209\n"
     ]
    }
   ],
   "source": [
    "cols = cols[:22] \n",
    "drop_columns = [i for i in corrmat.columns if i not in cols]\n",
    "\n",
    "print drop_columns , \"\\n\"\n",
    "X_train = X_train.drop(drop_columns, axis=1)\n",
    "print len(X_train.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_test = features_test.drop(drop_columns, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 网格搜索调参"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import fbeta_score, make_scorer, r2_score ,mean_squared_error\n",
    "\n",
    "def rmsle(y, y_pred):\n",
    "    return np.sqrt(mean_squared_error(y, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 随机森林"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 模型：RandomForest\n",
    "# 导入Grid\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# 初始化回归模型\n",
    "reg = RandomForestRegressor(random_state=42)\n",
    "\n",
    "# 确定参数列表\n",
    "parameters = {\n",
    "    'max_leaf_nodes': range(20, 100 ,10)\n",
    "}\n",
    "\n",
    "# 确定评分标准\n",
    "scorer = 'neg_mean_squared_error'\n",
    "\n",
    "# 回归模型使用网格搜索\n",
    "grid_reg = GridSearchCV(reg, parameters, scoring = scorer)\n",
    "\n",
    "# 训练\n",
    "grid_reg.fit(X_train, y_train)\n",
    "\n",
    "grid_reg.grid_scores_\n",
    "\n",
    "# 获得最佳拟合回归器\n",
    "best_reg_rf = grid_reg.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
      "           max_features='auto', max_leaf_nodes=80,\n",
      "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "           min_samples_leaf=1, min_samples_split=2,\n",
      "           min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
      "           oob_score=False, random_state=42, verbose=0, warm_start=False)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.095755002612566462"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print grid_reg.best_estimator_\n",
    "print grid_reg.best_estimator_\n",
    "pred_rf = best_reg_rf.predict(X_train)\n",
    "rmsle(pred_rf, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lasso回归"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "一般来说，对于高维的特征数据，尤其线性关系是稀疏的，我们会采用Lasso回归。或者是要在一堆特征里面找出主要的特征，那么Lasso回归更是首选了。但是Lasso类需要自己对α调优，所以不是Lasso回归的首选，一般用到的是下一节要讲的LassoCV类。\n",
    "\n",
    "https://www.cnblogs.com/pinard/p/6026343.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 模型：线性回归\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# 初始化回归模型\n",
    "reg = Lasso(alpha=0.001, random_state= 42)\n",
    "\n",
    "# 确定参数列表\n",
    "parameters = {\n",
    "    'normalize': [True, False],\n",
    "    'alpha': [0, 0.0001, 0.0005, 0.001]\n",
    "}\n",
    "\n",
    "# 确定评分标准\n",
    "scorer = 'neg_mean_squared_error'\n",
    "\n",
    "# 回归模型使用网格搜索\n",
    "grid_reg = GridSearchCV(reg, parameters, scoring = scorer)\n",
    "\n",
    "# 训练\n",
    "grid_reg.fit(X_train, y_train)\n",
    "\n",
    "# 获得最佳拟合回归器\n",
    "best_reg_lasso = grid_reg.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lasso(alpha=0.0005, copy_X=True, fit_intercept=True, max_iter=1000,\n",
      "   normalize=False, positive=False, precompute=False, random_state=42,\n",
      "   selection='cyclic', tol=0.0001, warm_start=False)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.10316908005607042"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print grid_reg.best_estimator_\n",
    "pred_lasso = best_reg_lasso.predict(X_train)\n",
    "rmsle(pred_lasso, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ElasticNet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "弹性网络，结合L1和L2正则化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg = ElasticNet(alpha=0.0005, l1_ratio=.9, random_state = 42)\n",
    "\n",
    "parameters = {\n",
    "    'alpha':[0, 0.0001, 0.0005, 0.001],\n",
    "    'l1_ratio':np.arange(0, 1, 0.1)\n",
    "}\n",
    "\n",
    "scorer = 'neg_mean_squared_error'\n",
    "\n",
    "grid_reg = GridSearchCV(reg, parameters, scoring= scorer)\n",
    "\n",
    "grid_reg.fit(X_train, y_train)\n",
    "\n",
    "best_elasticNet_reg =  grid_reg.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ElasticNet(alpha=0.0005, copy_X=True, fit_intercept=True,\n",
      "      l1_ratio=0.90000000000000002, max_iter=1000, normalize=False,\n",
      "      positive=False, precompute=False, random_state=42,\n",
      "      selection='cyclic', tol=0.0001, warm_start=False)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.10268429346808497"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print grid_reg.best_estimator_\n",
    "pred_elasticNet_reg = best_elasticNet_reg.predict(X_train)\n",
    "rmsle(pred_elasticNet_reg, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### SVR_rbf "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVR\n",
    "\n",
    "reg = SVR(kernel = 'rbf')\n",
    "\n",
    "parameters = {\n",
    "    'C': np.arange(1.1,2,0.1),\n",
    "    'gamma':np.arange(0,0.1,0.01)\n",
    "}\n",
    "\n",
    "scorer = 'neg_mean_squared_error'\n",
    "\n",
    "grid_reg = GridSearchCV(reg, parameters, scoring = scorer)\n",
    "\n",
    "grid_reg.fit(X_train, y_train)\n",
    "\n",
    "best_reg_SVR = grid_reg.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVR(C=1.1000000000000001, cache_size=200, coef0=0.0, degree=3, epsilon=0.1,\n",
      "  gamma=0.01, kernel='rbf', max_iter=-1, shrinking=True, tol=0.001,\n",
      "  verbose=False)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.10316908005607042"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print grid_reg.best_estimator_\n",
    "pred_lasso = best_reg_lasso.predict(X_train)\n",
    "rmsle(pred_lasso, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Xgboost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1、确定学习速率和tree_based 参数调优的估计器数目"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "code_folding": [
     0
    ],
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# # 确定最佳决策树的数量, xgboost中的cv函数来确定最佳的决策树数量, 提高Xgboost调参速度\n",
    "# # 后边有 early_stopping_rounds 个rmse没有下降的就停止\n",
    "# # 原话：\n",
    "# # Activates early stopping. CV error needs to decrease at least\n",
    "# #        every <early_stopping_rounds> round(s) to continue.\n",
    "# #        Last entry in evaluation history is the one from best iteration.\n",
    "\n",
    "# import xgboost as xgb\n",
    "# from xgboost.sklearn import XGBRegressor\n",
    "\n",
    "# def modelfit(alg, X_train, y_train, useTrainCV=True, cv_folds=5, early_stopping_rounds=50):\n",
    "    \n",
    "#     if useTrainCV:\n",
    "#         xgb_param = alg.get_xgb_params()\n",
    "#         xgtrain = xgb.DMatrix(X_train.values, label= y_train.values)\n",
    "#         cvresult = xgb.cv(xgb_param, xgtrain, num_boost_round=alg.get_params()['n_estimators'], nfold=cv_folds,\n",
    "#             metrics='rmse', early_stopping_rounds = early_stopping_rounds)\n",
    "#         alg.set_params(n_estimators=cvresult.shape[0])\n",
    "#         print cvresult\n",
    "\n",
    "#     #Fit the algorithm on the data\n",
    "#     alg.fit(X_train, y_train,eval_metric='rmse')\n",
    "\n",
    "#     #Predict training set:\n",
    "#     dtrain_predictions = alg.predict(X_train)\n",
    "\n",
    "#     # 模型训练报告\n",
    "#     print \"\\nModel Report\"\n",
    "#     print \"RMSE Score (Train): %f\" % rmse(y_train, dtrain_predictions)\n",
    "    \n",
    "#     # 特征重要性\n",
    "#     feat_imp = pd.Series(alg.booster().get_fscore()).sort_values(ascending=False)[:10,]\n",
    "#     feat_imp.plot(kind='bar', title='Feature Importances')\n",
    "#     plt.ylabel('Feature Importance Score')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 模型：Xgboost\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "best_reg_xgb = XGBRegressor(learning_rate= 0.01, n_estimators = 5000, \n",
    "                    max_depth= 4, min_child_weight = 1.5, gamma = 0.1, \n",
    "                   subsample = 0.7, colsample_bytree = 0.6, \n",
    "                   seed = 27)\n",
    "\n",
    "# modelfit(reg, X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0807258741724\n"
     ]
    }
   ],
   "source": [
    "best_reg_xgb.fit(X_train, y_train)\n",
    "pred_y_XGB = best_reg_xgb.predict(X_train)\n",
    "print rmsle(pred_y_XGB, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "学习速率为0.1时，理想的决策树数目为115个"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# rmsle_cv(best_reg_xgb, X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2、调整Xgboost相关参数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### max_depth 和 min_weight 参数调优"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "max_depth 树的最大深度\n",
    "\n",
    "min_child_weight 最小叶子节点样本权重和：\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "code_folding": [
     0
    ],
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# param_test1 = {\n",
    "#     'max_depth': range(3,10,2),\n",
    "#     'min_child_weight': range(1,6,2)\n",
    "# }\n",
    "\n",
    "# scorer = make_scorer(rmse)\n",
    "\n",
    "# # 负均方误差\n",
    "# grid_reg1 = GridSearchCV(estimator=XGBRegressor(learning_rate=0.01, n_estimators=1605, \n",
    "#                                                 max_depth=5, min_child_weight = 1,gamma = 0,\n",
    "#                                                 subsample = 0.8, colsample_bytree = 0.8, seed = 27), \n",
    "#                          param_grid = param_test1, scoring = 'neg_mean_squared_error')\n",
    "# grid_reg1.fit(X_train, y_train)\n",
    "# grid_reg1.grid_scores_, grid_reg1.best_params_, grid_reg1.best_score_\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "理想max_depth = 5， min_child_weight = 3，值附近细化调整"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "code_folding": [
     0
    ],
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# param_test1 = {\n",
    "#     'max_depth': [4, 5, 6],\n",
    "#     'min_child_weight': np.arange(1.0, 4.0, 0.5)\n",
    "# }\n",
    "\n",
    "# scorer = make_scorer(rmse)\n",
    "\n",
    "# # 负均方误差\n",
    "# grid_reg1 = GridSearchCV(estimator=XGBRegressor(learning_rate=0.1, n_estimators=115, \n",
    "#                                                 max_depth=5, min_child_weight = 1,gamma = 0,\n",
    "#                                                 subsample = 0.8, colsample_bytree = 0.8, seed = 27), \n",
    "#                          param_grid = param_test1, scoring = 'neg_mean_squared_error')\n",
    "# grid_reg1.fit(X_train, y_train)\n",
    "# grid_reg1.grid_scores_, grid_reg1.best_params_, grid_reg1.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "'max_depth'理想值 4, 'min_child_weight' 理想值 1.5 交叉验证得分：0.016"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### gamma参数调优"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "节点分裂所需的最小损失函数下降值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "# param_test3 = {\n",
    "#     'gamma' : [i/10.0 for i in range(0,5)]\n",
    "# }\n",
    "\n",
    "\n",
    "# grid_reg1 = GridSearchCV(estimator=XGBRegressor(learning_rate=0.1, n_estimators=115, \n",
    "#                                                 max_depth=4, min_child_weight = 1.5,gamma = 0,\n",
    "#                                                 subsample = 0.8, colsample_bytree = 0.8, seed = 27), \n",
    "#                          param_grid = param_test3, scoring = 'neg_mean_squared_error')\n",
    "# grid_reg1.fit(X_train, y_train)\n",
    "# grid_reg1.grid_scores_, grid_reg1.best_params_, grid_reg1.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "学习速率为0.1的情况下，理想决策树115， 'max_depth'理想值 4, 'min_child_weight' 理想值 1.5， gamma为0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 调整subsample 和 colsample_bytree 参数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "样本抽样和列抽样的比重"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "code_folding": [
     0
    ],
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# param_test4 = {\n",
    "#     'subsample':np.arange(0.5, 1.0 ,0.05),\n",
    "#     'colsample_bytree':np.arange(0.5, 1.0, 0.05)\n",
    "# }\n",
    "\n",
    "\n",
    "# grid_reg1 = GridSearchCV(estimator=XGBRegressor(learning_rate=0.1, n_estimators=115, \n",
    "#                                                 max_depth=4, min_child_weight = 1.5,gamma = 0,\n",
    "#                                                 subsample = 0.8, colsample_bytree = 0.8, seed = 27), \n",
    "#                          param_grid = param_test4, scoring = 'neg_mean_squared_error')\n",
    "# grid_reg1.fit(X_train, y_train)\n",
    "# grid_reg1.grid_scores_, grid_reg1.best_params_, grid_reg1.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "学习速率为0.1的情况下，理想决策树115， 'max_depth'理想值 4, 'min_child_weight' 理想值 1.5， gamma为0, \n",
    "\n",
    "colsample_bytree为0.6， subsample为0.7，MSE为0.0156"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3、正则化参数调优"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "# param_test5 = {\n",
    "#     \"reg_alpha\":np.arange(0.0, 1.1, 0.1),\n",
    "#     \"reg_lambda\":np.arange(0.0, 1.1, 0.1)\n",
    "# }\n",
    "\n",
    "# grid_reg1 = GridSearchCV(estimator=XGBRegressor(learning_rate=0.1, n_estimators=115, \n",
    "#                                                 max_depth=4, min_child_weight = 1.5,gamma = 0,\n",
    "#                                                 subsample = 0.7, colsample_bytree = 0.6, seed = 27), \n",
    "#                          param_grid = param_test5, scoring = 'neg_mean_squared_error')\n",
    "# grid_reg1.fit(X_train, y_train)\n",
    "# grid_reg1.grid_scores_, grid_reg1.best_params_, grid_reg1.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "学习速率为0.1的情况下，理想决策树115， \n",
    "\n",
    "'max_depth'理想值 4, 'min_child_weight' 理想值 1.5， gamma为0,\n",
    "\n",
    "colsample_bytree为0.6， subsample为0.7\n",
    "\n",
    "alpha为0.1，lambda为0\n",
    "\n",
    "MSE为0.0154"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4、降低学习速率，确定理想参数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用较低的学习速率，更多的决策树"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "code_folding": [
     0
    ],
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# reg2 = XGBRegressor(learning_rate=0.1, n_estimators= 1000, \n",
    "#                          max_depth=4, min_child_weight = 1.5,gamma = 0,\n",
    "#                         subsample = 0.7, colsample_bytree = 0.6, seed = 27)\n",
    "\n",
    "# modelfit(reg2, X_train, y_train )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "# pred_y_test = reg2.predict(X_test)\n",
    "# rmse(pred_y_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "学习速率为0.01的情况下，理想决策树5000， \n",
    "\n",
    "'max_depth'理想值 4, 'min_child_weight' 理想值 1.5， gamma为0,\n",
    "\n",
    "colsample_bytree为0.6， subsample为0.7\n",
    "\n",
    "alpha为0.1，lambda为0\n",
    "\n",
    "MSE为"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1、仅靠参数的调整和模型的小幅优化，想要让模型的表现有个大幅度提升是不可能的\n",
    "\n",
    "2、要想让模型的表现有一个质的飞跃，需要依靠其他的手段：\n",
    "\n",
    "    1、特征工程(feature egineering) \n",
    "    \n",
    "    2、模型组合(stacking)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 模型融合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mlxtend.regressor import StackingRegressor\n",
    "\n",
    "# metal_reg = Lasso(alpha= 0.0, random_state=42)\n",
    "metal_reg = SVR(kernel= 'rbf', C = 20)\n",
    "\n",
    "stregr = StackingRegressor(regressors = [ best_reg_lasso, best_elasticNet_reg], meta_regressor = metal_reg)\n",
    "\n",
    "# params = {'meta-lasso__alpha':[0.1, 1.0, 10.0] }\n",
    "\n",
    "# grid = GridSearchCV(estimator=stregr,\n",
    "#                     param_grid=params,\n",
    "#                     cv=5,\n",
    "#                     refit=True)\n",
    "\n",
    "# grid.fit(X_train, y_train)\n",
    "# for params, mean_score, scores in grid.grid_scores_:\n",
    "#     print(\"%0.3f +/- %0.2f %r\"\n",
    "#         % (mean_score, scores.std() / 2.0, params))\n",
    "\n",
    "\n",
    "# rmsle_cv(stregr, X_train, y_train).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.10139937264616997"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stregr.fit(X_train, y_train)\n",
    "stacked_y_pred  = stregr.predict(X_train)\n",
    "rmsle(y_train, stacked_y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 学习曲线"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用Sklearn中学习曲线函数：http://scikit-learn.org/stable/auto_examples/model_selection/plot_learning_curve.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "# print(__doc__)\n",
    "\n",
    "# import numpy as np\n",
    "# import matplotlib.pyplot as plt\n",
    "# from sklearn.naive_bayes import GaussianNB\n",
    "# from sklearn.svm import SVC\n",
    "# from sklearn.datasets import load_digits\n",
    "# from sklearn.model_selection import learning_curve\n",
    "# from sklearn.model_selection import ShuffleSplit\n",
    "\n",
    "\n",
    "# def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\n",
    "#                         n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):\n",
    "#     \"\"\"\n",
    "#     Generate a simple plot of the test and training learning curve.\n",
    "\n",
    "#     Parameters\n",
    "#     ----------\n",
    "#     estimator : object type that implements the \"fit\" and \"predict\" methods\n",
    "#         An object of that type which is cloned for each validation.\n",
    "\n",
    "#     title : string\n",
    "#         Title for the chart.\n",
    "\n",
    "#     X : array-like, shape (n_samples, n_features)\n",
    "#         Training vector, where n_samples is the number of samples and\n",
    "#         n_features is the number of features.\n",
    "\n",
    "#     y : array-like, shape (n_samples) or (n_samples, n_features), optional\n",
    "#         Target relative to X for classification or regression;\n",
    "#         None for unsupervised learning.\n",
    "\n",
    "#     ylim : tuple, shape (ymin, ymax), optional\n",
    "#         Defines minimum and maximum yvalues plotted.\n",
    "\n",
    "#     cv : int, cross-validation generator or an iterable, optional\n",
    "#         Determines the cross-validation splitting strategy.\n",
    "#         Possible inputs for cv are:\n",
    "#           - None, to use the default 3-fold cross-validation,\n",
    "#           - integer, to specify the number of folds.\n",
    "#           - An object to be used as a cross-validation generator.\n",
    "#           - An iterable yielding train/test splits.\n",
    "\n",
    "#         For integer/None inputs, if ``y`` is binary or multiclass,\n",
    "#         :class:`StratifiedKFold` used. If the estimator is not a classifier\n",
    "#         or if ``y`` is neither binary nor multiclass, :class:`KFold` is used.\n",
    "\n",
    "#         Refer :ref:`User Guide <cross_validation>` for the various\n",
    "#         cross-validators that can be used here.\n",
    "\n",
    "#     n_jobs : integer, optional\n",
    "#         Number of jobs to run in parallel (default 1).\n",
    "#     \"\"\"\n",
    "#     plt.figure()\n",
    "#     plt.title(title)\n",
    "#     if ylim is not None:\n",
    "#         plt.ylim(*ylim)\n",
    "#     plt.xlabel(\"Training examples\")\n",
    "#     plt.ylabel(\"Score\")\n",
    "#     train_sizes, train_scores, test_scores = learning_curve(\n",
    "#         estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n",
    "#     train_scores_mean = np.mean(train_scores, axis=1)\n",
    "#     train_scores_std = np.std(train_scores, axis=1)\n",
    "#     test_scores_mean = np.mean(test_scores, axis=1)\n",
    "#     test_scores_std = np.std(test_scores, axis=1)\n",
    "#     plt.grid()\n",
    "\n",
    "#     plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
    "#                      train_scores_mean + train_scores_std, alpha=0.1,\n",
    "#                      color=\"r\")\n",
    "#     plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
    "#                      test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n",
    "#     plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n",
    "#              label=\"Training score\")\n",
    "#     plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n",
    "#              label=\"Cross-validation score\")\n",
    "\n",
    "#     plt.legend(loc=\"best\")\n",
    "#     return plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "# title = \"Learning Curves (Xgboost)\"\n",
    "# # Cross validation with 100 iterations to get smoother mean test and train\n",
    "# # score curves, each time with 20% data randomly selected as a validation set.\n",
    "# cv = ShuffleSplit(n_splits=100, test_size=0.2, random_state=0)\n",
    "\n",
    "# estimator = XGBRegressor()\n",
    "# plot_learning_curve(estimator, title, features_train, labels_train, ylim=(0.7, 1.01), cv=cv, n_jobs=4)\n",
    "\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 预测目标数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 123213.61828874,  167839.28733335,  192632.51044019, ...,\n",
       "        168755.41285439,  127345.89201239,  239976.52796591])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred = np.expm1(stregr.predict(features_test) * 0.5 + best_reg_xgb.predict(features_test) * 0.5)\n",
    "pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 输出结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>1485</th>\n",
       "      <td>185413.056070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1486</th>\n",
       "      <td>202427.635160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1487</th>\n",
       "      <td>337400.760128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1488</th>\n",
       "      <td>239184.316300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1489</th>\n",
       "      <td>198154.808960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1490</th>\n",
       "      <td>225520.811859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2890</th>\n",
       "      <td>85590.857387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2891</th>\n",
       "      <td>144087.758371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2892</th>\n",
       "      <td>43872.722144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2893</th>\n",
       "      <td>75184.978056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2894</th>\n",
       "      <td>49199.724927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2895</th>\n",
       "      <td>320393.815873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2896</th>\n",
       "      <td>282580.982118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2897</th>\n",
       "      <td>226077.164412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2898</th>\n",
       "      <td>158581.590557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2899</th>\n",
       "      <td>221672.008225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2900</th>\n",
       "      <td>166162.673885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2901</th>\n",
       "      <td>216060.363770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2902</th>\n",
       "      <td>190867.851564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2903</th>\n",
       "      <td>351148.090199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2904</th>\n",
       "      <td>361479.843399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2905</th>\n",
       "      <td>85339.662024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2906</th>\n",
       "      <td>197283.223457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2907</th>\n",
       "      <td>114218.338655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2908</th>\n",
       "      <td>132395.779202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2909</th>\n",
       "      <td>159561.868155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2910</th>\n",
       "      <td>83509.493429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2911</th>\n",
       "      <td>86165.314283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2912</th>\n",
       "      <td>151611.983613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2913</th>\n",
       "      <td>90319.885493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2914</th>\n",
       "      <td>82657.824240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2915</th>\n",
       "      <td>90031.113497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2916</th>\n",
       "      <td>90434.373243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2917</th>\n",
       "      <td>168755.412854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2918</th>\n",
       "      <td>127345.892012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2919</th>\n",
       "      <td>239976.527966</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1459 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          SalePrice\n",
       "Id                 \n",
       "1461  123213.618289\n",
       "1462  167839.287333\n",
       "1463  192632.510440\n",
       "1464  200991.557836\n",
       "1465  197575.109634\n",
       "1466  175907.456191\n",
       "1467  178438.846336\n",
       "1468  166813.516425\n",
       "1469  197072.605923\n",
       "1470  123141.372029\n",
       "1471  200320.342783\n",
       "1472   99944.960358\n",
       "1473   99027.367851\n",
       "1474  153555.070351\n",
       "1475  117122.027853\n",
       "1476  396007.666553\n",
       "1477  255897.266031\n",
       "1478  296374.984662\n",
       "1479  300706.176897\n",
       "1480  527262.280452\n",
       "1481  334774.353462\n",
       "1482  214718.275972\n",
       "1483  180503.720344\n",
       "1484  168910.600778\n",
       "1485  185413.056070\n",
       "1486  202427.635160\n",
       "1487  337400.760128\n",
       "1488  239184.316300\n",
       "1489  198154.808960\n",
       "1490  225520.811859\n",
       "...             ...\n",
       "2890   85590.857387\n",
       "2891  144087.758371\n",
       "2892   43872.722144\n",
       "2893   75184.978056\n",
       "2894   49199.724927\n",
       "2895  320393.815873\n",
       "2896  282580.982118\n",
       "2897  226077.164412\n",
       "2898  158581.590557\n",
       "2899  221672.008225\n",
       "2900  166162.673885\n",
       "2901  216060.363770\n",
       "2902  190867.851564\n",
       "2903  351148.090199\n",
       "2904  361479.843399\n",
       "2905   85339.662024\n",
       "2906  197283.223457\n",
       "2907  114218.338655\n",
       "2908  132395.779202\n",
       "2909  159561.868155\n",
       "2910   83509.493429\n",
       "2911   86165.314283\n",
       "2912  151611.983613\n",
       "2913   90319.885493\n",
       "2914   82657.824240\n",
       "2915   90031.113497\n",
       "2916   90434.373243\n",
       "2917  168755.412854\n",
       "2918  127345.892012\n",
       "2919  239976.527966\n",
       "\n",
       "[1459 rows x 1 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 增加索引，列名，构建DataFrame，符合输出数据格式\n",
    "\n",
    "test_df = pd.read_csv('test.csv')\n",
    "predict_df = pd.DataFrame({'Id': test_df['Id'], 'SalePrice': pred})\n",
    "\n",
    "# DataFrame设定index\n",
    "predict_df = predict_df.set_index('Id')\n",
    "\n",
    "# 重命名DataFrame的列，列名 = 字典{原：替换后}\n",
    "# predict_df.rename(columns = {predict_df.columns[0]: 'Id'}, inplace=True)\n",
    "\n",
    "predict_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict_df.to_csv('Submission.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 待优化策略"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "ROUND 1：\n",
    "\n",
    "I：基础机器学习框架搭建,采用Random Forest 0.18129 \n",
    "\n",
    "II：Xgboost + 所有特征: 0.14339 ⬆️（可解释性差，容易过拟合）\n",
    "\n",
    "III：只取相关性强数值特征 ：0.14870 ⬇️\n",
    "\n",
    "IV：相关性强数值特征 + Label Coder ：0.14628 ⬇️\n",
    "\n",
    "V: 部分数值特征转换为类别特征：Msubclass等：0.13902 ⬆️\n",
    "\n",
    "VI：部分类别特征Label coder有序；部分独热编码：0.13814 ⬆️\n",
    "\n",
    "VII：处理倾斜特征和目标变量：0.13806 ⬆️\n",
    " \n",
    "ROUND2：\n",
    "\n",
    "I：XGboost调参 0.1325 -> 0.131 -> 0.12907  ⬆️\n",
    "\n",
    "II：多种算法stacking：0.12818 ⬆️\n",
    "\n",
    "III：前10有效特征，融合stacking和xgb：0.12584 ⬆️\n",
    "\n",
    "IIII：过拟合较严重，简化模型，stacking去除RF：0.12138 ⬆️\n",
    "\n",
    "\n",
    "待优化点有：\n",
    "\n",
    "1、评分标准更改为Kaggle标准 ✅\n",
    "\n",
    "2、特征分析，创造新特征 ✅\n",
    "\n",
    "特征较多：74，如何选取以及探索恰当的特征？\n",
    "\n",
    "3、探索性分析，确定关键特征 ✅\n",
    "\n",
    "【探索性数据分析】\n",
    "\n",
    "4、Xgboost学习，调参  ✅\n",
    "\n",
    "5、集成方法 stacking   ✅\n",
    "\n",
    "6、模型有些过拟合，试图减少特征的数量 ✅\n",
    "\n",
    "\n",
    "待解决问题有：\n",
    "\n",
    "1、怎么判断模型存在过拟合？✅\n",
    "\n",
    "学习曲线，Learning curve\n",
    "\n",
    "2、线性回归为什么有负值 ✅\n",
    "\n",
    "在做类别数据 -> 数值转化时，训练集同测试集列名存在区别。只新增了测试集缺失的列，未调整列的顺序。\n",
    "注意：test = test[train.columns]\n",
    "\n",
    "3、XGboost调参技巧 ✅"
   ]
  }
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